Course Announcements Archive Through 2009

Last revised: 2/18/09

SPRING 2009

STATISTICS 20000. Elementary Statistics.
Sec 01: Naiqing Gu, MWF, 9:30-10:20 AM, Eckhart 133.
Sec 02: Xiaohui Chang, MWF, 12:30-1:20 PM, Eckhart 133.
PQ: Students with credit for Stat 220000 or 23400 not admitted.
Required Reading: Freedman, Pisani, and Purves, Statistics, 4th edition, W. W. Norton Press. ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This
course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000=HDCP 22050. Stat Meth And Applications.
Sec 01: Debashis Mondal, MWF, 10:30–11:20 AM, Eckhart 133.
Sec 02: Winfried Barta, MWF, 1:30-2:20 PM, Eckhart 133.
PQ: 2 qtrs Calculus. Students who matriculate in the College after August 2008 may count either Stat 22000 or 23400, but not both, toward the 42 credits required for graduation.
Required Reading: Moore and McCabe, Introduction to the Practice of Statistics, 5th edition, W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007.

This course introduces statistical techniques and methods of data analysis, including the use of computers. Examples are drawn from the biological, physical, and social sciences. Students are required to apply the techniques discussed to data drawn from actual research. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, one- and two-sample problems, the analysis of variance, linear regression, and analysis of discrete data.

STATISTICS 22200. Linear Models And Exper Design.
Sec 01: Linda Collins, MWF, 11:30 AM–12:20 PM.
PQ: Stat 22000 or consent of instructor.
Required Reading: Oehlert, G. W. (2000). A First Course in Design and Analysis of Experiments, W. H. Freeman. ISBN-10: 0-7167-3510-5 ISBN-13: 978-0-7167-3510-6.

This course covers principles and techniques for the analysis of experimental data and the planning of the statistical aspects of experiments. Topics include linear models, analysis of variance, randomization, blocking, factorial designs, confounding, and incorporation of covariate information. In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment.

STATISTICS 23400. Statistical Models/Method-1.
Sec 01: David Degras, MWF, 11:30 AM–12:20 PM, Eckhart 133.
Sec 02: David Degras, MWF, 2:30–3:20 PM, Eckhart 133.
PQ: Math 13300, 15300 or 16300. Students who matriculate in the College after August 2008 may count either Stat 22000 or 23400, but not both, toward the 42 credits required for graduation.
Required Reading: Rossman (2006). Investigating Statistical Concepts, Applications, and Methods by Chance, Duxbury (Thomson Brooks/Cole). ISBN-10: 0495050644, ISBN-13: 978-D495050643.
Tanis and Hogg (2007). A Brief Course in Mathematical Statistics, Prentice Hall, ISBN-10: 0131751395, ISBN-13: 978-0131751392.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data.
Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

Univariate calculus and computer simulation are used throughout the course to investigate statistical concepts and their mathematical underpinnings. One full year of univariate calculus is a prerequisite for the course (Math 13300, 15300, or 16300). Familiarity with at least limits, derivatives and integrals of polynomial and exponential functions, change of variable (substitution) in definite integrals, max-min problems, use of summation notation, and sequences and series as well as a willingness to explore ideas mathematically are key to your success in this course. See http://www.stat.uchicago.edu/~stat234 for more detailed information. Stat 23400 takes the mathematical prerequisite quite seriously. Enrolling concurrently in either Math 13300, 15300, or 16300 while taking Stat 23400 is very strongly
discouraged. Further, students who do not feel strong mathematically, may want to wait until completing their entire mathematical requirement (e.g., Math 19500-19600 for Economics majors) before enrolling in Stat 23400. Economics majors are strongly encouraged to delay taking Stat 23400 until the quarter just before enrolling in their required econometrics course (Econ 21000), for which Stat 23400 is a prerequisite. Thus, delaying Stat 23400 until at least late in the second year or even early in the third year of the Economics degree program should not be considered unusual.

STATISTICS 24500. Statistical Theory/Method-2.
Sec 01: Debashis Mondal, TTH, 1:30 PM–2:50 PM, Eckhart 133.
PQ Multivariate Calculus (Math 19520 or 20000 or equivalent) and Linear Algebra (Math 19620 or 25500 or Stat 24300 or equivalent).
Required Reading: Rice, John A. (2007). Mathematical Statistics and Data Analysis, 3rd edition, Duxbury. ISBN-10: 0534399428, ISBN-13: 978-0534399429.

This is the second quarter of a two-quarter sequence. Enrollment in the second quarter alone is permitted, although not recommended. The first quarter covered the basics—tools from probability and the elements of statistical theory. The second quarter will cover statistical methodology, including the data transformation, regression phenomena, t-tests, analysis of variance, linear regression, correlation, and some multivariate distribution theory. Some principles of data analysis will be introduced, and an attempt will be made to present ANOVA and regression in a unified framework. Much of the material is covered in chapters 10–12 and 14 of the text, but other viewpoints and derivations will be introduced as well. The computer will be used in the second quarter. Some mathematical maturity will be assumed, to the level of calculus. The computer will be used for data analysis and simulation.

STATISTICS 24600. Statistical Theory/Method-3.
Sec 01: Yali Amit, TT, 10:30–11:50 AM, Eckhart 133.
PQ: Stat 24500 or equivalent or consent of instructor.
Required Reading: Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning, Springer Verlag. ISBN-10: 0387310738, ISBN-13: 978-0387310732.

This course will introduce a variety of modern statistical methods. We will start with the description of families of multivariate models—multivariate normal distribution, graphical models, log-linear models. We will then discuss inference for such models including the EM algorithm, Bayesian inference and Monte-carlo methods. The main application of the models will be in classification problems—i.e the 'generative approach'. We will also introduce some modern discriminative approaches for classification.

STATISTICS 25100. Intro to Math Probability.
Sec 01: Michael J. Wichura, TTH, 4:30–5:50 PM, Eckhart 133.
PQ: Math 20000 or Math 20500 or consent of instructor.
Required Reading: Ross, Sheldon (January 7, 2009). A First Course in Probability, 8th edition, Prentice Hall. ISBN-10: 013603313X, ISBN-13: 978-0136033134.

The aim of the course is to provide an introduction to the concepts of probability. The course will cover the basic ideas used to describe aspects of randomness, such as events, random variables, independence, and conditional probability, with emphasis on the methods, calculation, and applications of probability. The topics treated are: combinatorics; probability models; rules of probability; conditional probability; independence; random variables; expectation and standard deviation; games of chance; common discrete distributions—uniform, binomial, hypergeometric, geometric, negative binomial, and Poisson—and their interrelationships; univariate and multivariate density and distribution functions, change of variable formulas for densities, common continuous distributions—beta, Cauchy, chisquare, exponential, gamma, lognormal, normal, uniform—and their interrelationships; moment generating functions, laws of large numbers and the central limit theorem. For more information, please visit http://www.stat.uchicago.edu/~wichura/Stat251/courseinfo.html.

STATISTICS 26100=STAT 33600. Time Dependent Data.
Sec 01: Mei Wang, TTH, 1:30–2:50 PM, Eckhart 133.
PQ Stat 22400 or Stat 24500 or consent of instructor.
Required Reading: Shumway and Stoffer (2006). Time Series Analysis and Its Applications: With R Examples, 2nd edition, Springer. ISBN-10: 0387293175, ISBN-13: 978-
0387293172.

This course considers the modeling and analysis of data that are ordered in time. The main focus will be on quantitative observations taken at evenly spaced intervals and will include both time-domain and spectral approaches. Time permitting, statistical approaches to other data types, such as multiple time series, categorical observations or point processes, will be considered.

STATISTICS 26700=CHSS 32900, HIPS 25600, STAT 36700, History of Statistics.
Sec 01: Stephen M. Stigler, MWF, 9:30–10:20 AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: Stigler, Stephen M. (1990). The History of Statistics: The Measurement of Uncertainty Before 1900, Belknap Press of Harvard University Press. ISBN-10:
067440341X, ISBN-13: 978-0674403413. Other materials will be distributed in class or by web.

This course will cover topics in the history of statistics, from the eleventh century to the middle of the twentieth century. The emphasis will be upon the period 1650 to 1950, and upon the mathematical developments in the theory of probability and how they came to be used in the sciences, both to quantify uncertainty in observational data and as a conceptual framework for scientific theories. The course will include broad views of the development of the subject, and closer looks at specific people and investigations, including reanalyses of historical data. Topics will include: Early probability; Probability in seventeenth century medicine; Inverse probability in inference; Statistical methods in early geodesy; The introduction of least squares; Statistics in social science; Statistics in early biology and psychology; Simulation; Statistics and the evaluation of social programs; Maximum likelihood estimation; Statistics and nineteenth century forensic science; Statistics and medical science. Major figures who will be examined include Pascal, various Bernoullis, De Moivre, Laplace, Boscovich, Gauss, Quetelet, Galton, Edgeworth, Karl Pearson, Gosset, Fisher, Neyman.

STATISTICS 30200. Mathematical Statistics-2.
Sec 01: Matthew Stephens, MW, 1:30–2:50 PM, Eckhart 117.
PQ: Stat 30100 or consent of instructor.
Required Reading: Berger, James O., Statistical Decision Theory and Bayesian Analysis, 2nd edition.

This course continues the development of Mathematical Statistics, with an emphasis on Bayesian inference. Key topics include Decision theory, The Likelihood principle, Foundations and philosophy of probability, Prior information, exchangeability, De Finetti's theorem, Bayesian inference (estimation and hypothesis testing), Bayesian computation, admissibility and Stein's paradox. The mathematical level will generally be at that of an easy advanced calculus course. We will assume familiarity with standard statistical distributions (e.g. Normal, Poisson, Binomial, Exponential), with the laws of probability, expectation, conditional expectation etc. Concepts will be illustrated mainly by instructive "toy" examples, where calculations can be done by hand. However, we will also study more complex, practical applications of Bayesian statistics. Although methods of computation will be discussed, the primary focus will be on concepts, and not on computation.

STATISTICS 31300. Intro: Stochastic Processes-2.
Sec 01: Per A. Mykland, TTH, 10:30–11:50 AM, Eckhart 117.
PQ: Stat 31200 or consent of instructor.
Required Reading: Ross, S. (1996). Stochastic Processes, 2nd edition., Wiley.

This course is a continuation of Statistics 31300: Introduction to Stochastic Processes I. Topics to be discussed will include rates of convergence for Markov chains, introduction to MCMC, generating functions, Galton-Watson processes, continuous-time Markov chains, martingales, and Brownian motion. There will be weekly homework assignments, and midterm and final exams.

STATISTICS 33200=HSTD 43200. Causal Inference.
Sec 01: Tyler VanderWeele, Thu, 3:00–5:50 PM, BSLC arr.
PQ Stat 22400-22600 or HSTD 32400-32700 or equivalent or consent of instructor.
Required Reading: None.

The course will be concerned with the process of drawing causal inferences from observational data in the biomedical and social sciences. The course will introduce a number of fundamental concepts in causal inference and cover methods of estimating causal effects for both point- and time-varying exposures. Concepts and methods that will be covered include: confounding, potential outcomes, propensity scores, causal directed acyclic graphs, inverse probability of treatment weighting, marginal structural models, mediation, interaction, sensitivity analysis, and instrumental variables.

STATISTICS 33600=STATISTICS 26100. Time Dependent Data.
Sec 01: Mei Wang, TTH, 1:30 PM–2:50PM, Eckhart 202.
PQ: Stat 22400 or 24500 or consent of instructor.
Required Reading: Shumway and Stoffer (2006). Time Series Analysis and Its Applications: With R Examples, 2nd edition, Springer. ISBN-10: 0387293175, ISBN-13: 978-0387293172.

STATISTICS 33620. Statistical Methods for Spatial-Temporal Processes.
Sec 01: Michael L. Stein, TTH, 9:00–10:20 AM, Eckhart 117.
PQ: Consent of instructor.
Required Reading: None.

This course will investigate emerging approaches in the statistical modeling and analysis of large spatial-temporal datasets, with a focus on examples from the atmospheric and environmental sciences. Some of the topics we might cover include various extensions of the Kalman filter, hierarchical Bayesian approaches that incorporate physical understanding of the underlying process and spectral methods for spatial-temporal processes. Students will be expected to read papers in the current literature and discuss them in class. Classwork will include code development, likely done in groups and perhaps building on existing software.

STATISTICS 33700=BUSF 41914, ECON 31500. Multivariate Time Series Analylsis.
Sec 01: Ruey-Shiong Tsay, 8:30–11:30 AM.
PQ Business 41910=Stat 33700 and Econ 31500.
Required Reading: None.
Course website: http://faculty.chicagobooth.edu/ruey.tsay/teaching/mts
A useful web site for U.S. data: Fed. Res. at St Louis: http://research.stlouisfed.org/fred2/

Course Objective:

To study the basic theory of multivariate processes
To gain experience in analyzing multivariate time series data
To learn multivariate time series models, including vector AR and ARMA models with exogenous variables
To understand co-integration and error-correction models
To study structural specification of a vector process
To learn state-space models and Kalman filter.

No textbook is assigned. Lecture Notes available on course web.

Grading:

Mid-term (40%), Final Exam (40%), and homework assignments (20%).

Computing:

The key software packages are SCA and S-Plus, but you may use any software of your choice.

Course Outline: All topics include applications

Transfer function models
Stationary vector autoregressive and moving average models
Estimation, modeling, and forecasting
Unit-root, co-integration and error-correction models
Multivariate Seasonal models
Structural specification
State-space model and Kalman filter
Multivariate volatility models if time permits.

STATISTICS 34700. Generalized Linear Models.
Sec 01: Peter McCullagh, TTH, 3:00–4:20 PM, Eckhart 133.
PQ: Stat 34300 or consent of instructor.
Required Reading: Venables & Ripley, Modern Applied Statistics w/S, 4th edition, Springer. ISBN: 0-387-
95457-0.
Cox & Snell (2000). Applied Statistics, Chapman & Hall/CRC. ISBN: 0-412-16570-8.

This is an applied course for students who are familiar with linear models at the level of Draper and Smith or Weisberg. The following topics will be covered:

Factors, variates, contrasts, interactions
Exponential-family models: variance function
Definition of a generalized linear model: link functions
Analysis of deviance
Specific examples of GLMs
Logistic and probit regression
Cumulative logistic models
Log-linear models and contingency tables
Inverse linear models
Quasi-likelihood and least squares; estimating functions
Over-dispersion
Partially linear models.

STATISTICS 35500. Statistical Genetics.
Sec 01: Mary Sara McPeek, Fri, 1:30–4:00 PM, Eckhart 117.
PQ: Human Genetics 47100, Stat 24400-24500 or equivalent recommended. Students w/o this background should consult instructor.
Required Reading: There is no textbook.

This is an advanced course in statistical genetics. It is recommended that students have either Human Genetics 471 or Stat 244 and 245 as prerequisites. This is a discussion course and student presentations will be required. Topics vary and may include, but are not limited to, statistical problems in association mapping, linkage mapping, population genetics, microarray analysis, and genetic models for complex traits.

STATISTICS 36700=STAT 26700, CHSS 32900, HIPS 25600. History of Statistics.
Sec 01: Stephen M. Stigler, MWF, 9:30–10:20AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: Stigler, Stephen M. (1990). The History of Statistics: The Measurement of Uncertainty Before 1900, Belknap Press of Harvard University Press. ISBN-10: 067440341X, ISBN-13: 978-0674403413. Other materials will be distributed in class or by web.

STATISTICS 37710=CMSC 35400. Machine Learning.
Sec 01: Partha Niyogi, TTH, 12:00–1:20PM, Ryerson 276.
PQ: CMSC 25000 or 35000 or consent of instructor.

STATISTICS 38500=MATH 38500. Advanced Topics: Probability.
Sec 01: Steven Lalley, TTH, 3:30–4:20 PM, Eckhart 117.
PQ:
Required Reading None.

Topics for Statistics 38500 will include:

Continuous-time martingales
Brownian motion
Levy processes
Ito integral and stochastic calculus
Stochastic differential equations and diffusions

STATISTICS 43800. Statistical Inference for Financial Data.
Sec 01: Per A. Mykland, TTH, 1:30–2:50 PM, Eckhart 117.
Note Class starts May 5 (1/2 qtr).
PQ Stat 49210 or equivalent.
Required Reading: None.

This is a course in the analysis of high frequency, tick-by-tick data. The application is mainly in finance, but the course may also be of interest to other applications with high frequency data, such as neural science. We cover the estimation of volatility and related quantities, such as leverage effect and realized regressions, including under microstructure noise. Tools include process convergence and contiguity, with and without microstructure noise.

STATISTICS 49210=ECON 31401. Learning, Filtering and Pricing.
Sec 01: Lars P. Hansen, TTH, 1:30–2:50 PM, Eckhart 117.
Note This class meets 5 weeks only. Class starts on March 31.
PQ: STAT 39000 or equivalent.
Required Reading: TBA

This is a five-week course on structural economic models of asset prices. It will develop and apply martingale and statistical methods to contrast implications of alternative models. In additional it will explore the impact of learning and model uncertainty on prices in decentralized asset markets.

WINTER 2009

College Courses

STATISTICS 20000. Elementary Statistics.
Sec 01: Wei Biao Wu, MWF 9:30-10:20 AM, Eckhart 133.
PQ: Math 10600 or equivalent.
Required reading: Freedman, Pisani, and Purves, Statistics, 4th edition, W. W. Norton Press. ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000. Statistical Methods and Their Applications.
Sec 01: Peter McCullagh, MWF 10:30-11:20 AM, Eckhart 133.
PQ: 2 QTRS Calculus.
Required reading: Moore and McCabe, Introduction to the Practice of Statistics, 5th edition. W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007.

STATISTICS 22600=HSTD 32600. Analysis of Categorical Data.
Sec 01: Mei Wang, TTH, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 22000 or equivalent.
Required reading: Agresti, A. (2007). An introduction to Categorical Data Analysis, 2nd edition, Wiley.

It is expected that the students have a good understanding of basic descriptive statistics such as means, variances and expectation, of the inferential notions of estimate, confidence intervals and significance or hypothesis testing. Familiarity with one statistical package, e.g. R, Splus, SAS, SPSS, Stata or Minitab, and ability to access Web sites and to download files from the Web are required. The free statistical package R will be used in this course for Winter 2007.

This course is an introduction to the theory and applications of statistical methods for investigating the relationships among discrete variables. The course will present methods for analyzing categorical data, including standard methods for contingency tables such as odds ratios,
tests of independence and various measures of association, generalized linear models for binary data and count data, logistic regression for binomial data, loglinear models for Poisson data, and models for paired samples with categorical responses. The statistical techniques discussed will be presented by many real examples involving physical, biological and social science data.

STATISTICS 22700=HSTD32700. Biostatistical Methods.
Sec 01: Ronald A. Thisted, TTH, 10:30-11:50 AM, BSLC room to be announced.
PQ: HSTD 32400/STAT 22400 or STAT 24500 or equivalent; or consent of instructor.
Required Reading: Collett, D. (2003). Modelling Binary Data, 2nd edition, Boca Raton, Chapman & Hall/CRC.
Collett, D. (2004). Modelling Survival Data in Medical Research, 2nd edition, London, Chapman & Hall.

This course is designed to provide students with tools for analyzing categorical, count, and time-to-event data frequently encountered in medicine, public health and related biological and social sciences. The course will emphasize application of methods rather than statistical theory, including recognition of the appropriate methods, interpretation and presentation of results. Methods covered include: contingency table analysis, logistic regression, log-linear (Poisson) regression, conditional logistic regression, regression methods for ordinal data, Kaplan-Meier survival curves, parametric survival models, and Cox proportional-hazards survival analysis.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Nina Singhal Hinrichs, TTH, 9:00-10:20 AM,Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300.
Required Reading: Chance and Rossman (2005). Investigating Statistical Concepts, Applications, and Methods, 1st edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.
Tanis and Hogg (2008). A Brief Course in Mathematical Statistics, Pearson/Prentice Hall, ISBN: 0-1317-5139-5.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

Stat 23400 takes the mathematical prerequisite quite seriously. Enrolling concurrently in either Math 13300, 15300, or 16300 while taking Stat 23400 is very strongly discouraged. Further, students who do not feel strong mathematically, may want to wait until completing their entire mathematical requirement (e.g., Math 19500-19600 for Economics majors) before enrolling in Stat 23400. Economics majors are strongly encouraged to delay taking Stat 23400 until the quarter just before enrolling in their required econometrics course (Econ 21000), for which Stat 23400 is a prerequisite. Thus, delaying Stat 23400 until at least late in the second year or even early in the third year of the Economics degree program should not be considered unusual.

STATISTICS 24300. Numerical Linear Algebra.
Sec 01: Lynne Butler, MW, 3:00-4:20 PM, Pick 016.
PQ: Multivariate Calculus (MATH 19520 or 20000 or equivalent).
Required Reading: Strang, Gilbert (2006). Linear Algebra and Its Applications, 4th edition, Brooks/Cole.

In this course students learn the ideas and methods of linear algebra by understanding them geometrically, justifying them algebraically, and using them to solve problems in various disciplines. The ideas and methods covered are used in advanced courses on quantum mechanics, econometrics, and linear statistical models. Topics covered include: Gaussian elimination; vector spaces and orthogonality; eigenvectors and eigenvalues; diagonalization of real symmetric (and complex Hermitian) matrices; the spectral theorem; QR, Cholesky and Singular Value Decompositions.

This is a first course in linear algebra but, unlike matrix algebra courses, it prepares students for graduate work in statistics, physical chemistry, physics and economics.

STATISTICS 24400. Statistical Theory and Methods I.
Sec 01: Stephen Stigler, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: MATH 19600, 20100, or 20500.
Recommended reading: Detailed notes will be available in the first quarter. The text is Rice, John A., Mathematical Statistics and Data Analysis, 3rd edition, Duxbury.

This is the first quarter of a two-quarter sequence. Enrollment in the first quarter alone is permitted, although not recommended. The first quarter will cover the basics -- tools from probability and the elements of statistical theory. Topics will include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distribution, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions and chi-square tests. Some large sample theory will be included. The emphasis will be upon statistical theory, specifically upon concepts and tools that are useful for understanding and applying statistical methodology. Examples will be drawn from the social, physical, and biological sciences. There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited and brief, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation. The computer will be used in the second quarter.

STATISTICS 24500. Statistical Theory and Methods II.
Sec 01: Wei Biao Wu, TTH, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 24400 or consent of instructor.
Required reading: Rice, J. A. (2006). Mathematical Statistics and Data Analysis, 3rd edition, Brooks/Cole.

This is the second quarter of a two-quarter sequence. Enrollment in the second quarter alone is permitted, although not recommended. The first quarter covered the basics -- tools from probability and the elements of statistical theory. The second quarter will cover statistical methodology, including the data transformation, regression phenomena, t-tests, analysis of variance, linear regression, correlation, and some multivariate distribution theory. Some principles of data analysis will be introduced, and an attempt will be made to present ANOVA and regression in a unified framework. Much of the material is covered in chapters 10-12 and 14 of the text, but other viewpoints and derivations will be introduced as well. The computer will be used in the second quarter. Some mathematical maturity will be assumed, to the level of calculus. The computer will be used for data analysis and simulation.

STATISTICS 24700=CPNS 32100. Math/Stats Methods for Neuroscience-2.
Sec 01: William Van Drongelen, WF, 1:30-2:50 PM, BSLC 401.
PQ: STAT 24400 or consent of instructor.
Required reading: Rice, J. A. (1995). Mathematical Statistics and Data Analysis, 2nd edition, Duxbury.

This course deals with the application of non-linear methods in signal processing and dynamical systems theory to issues in neuroscience. Data analysis with Matlab is again emphasized.

The third course in this sequence is an elective course in one of the quantitative sciences relevant to neuroscience that can be selected by the student in consultation with the program chair.

CANCELLED
STATISTICS 25200=STAT 31200. Introduction to Stochastic Processes I.

STATISTICS 25300=STAT 31700. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 117.
PQ: STAT 25100 or STAT 24400 or equivalent. Consent of instructor.
Required reading: Ross, R., (2007). Introduction to Probability Models, 9th edition.

This course introduces stochastic processes as models for a variety of phenomena in the physical and biological sciences. Another appropriate title for the course could be "an Introduction to Applied Stochastic Processes." Following a very brief review of basic concepts in probability the course will introduce stochastic processes that are popular in applications in sciences, such as discrete time Markov chain, the Poisson process, continuous time Markov process, renewal process and Brownian motion.

Graduate Courses

STATISTICS 30100. Mathematical Statistics I.
Sec 01: Mary Sara McPeek, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30400 or consent of instructor.
Required Reading: Casella and Berger, Statistical Inference, 2nd edition.
Ferguson, A Course in Large Sample Theory.

This course is part of a two-quarter sequence on the theory of statistics. Topics will include exponential families, quadratic forms of multivariate normal, asymptotics of order statistics, sufficiency and completeness, the likelihood function, methods of point estimation, and asymptotic properties of maximum likelihood estimates. Other topics (e.g. Bayesian methods and methods for dependent observations) may be covered if time permits.

STATISTICS 31200. Introduction to Stochastic Processes 1.
Sec 01: Steven P. Lalley, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: STAT 25100 or consent of instructor.
Required reading: Ross, S. (1996). Stochastic Processes, 2nd edition, Wiley.

Stochastic processes provide models for random events that evolve in time and may include substantial dependence among observations at different times. The goal of this course is to present a variety of useful models including Markov chains, renewal theory, random walks, queueing and branching processes.

STATISTICS 31700=STAT 25300. Introduction to Probability Models.
Sec 01: Mei Wang, TTh, 9:00-10:20 AM, Eckhart 117.
PQ: STAT 24400 or STAT 25100.
Required reading: Ross, R., (2007). Introduction to Probability Models, 9th edition.

STATISTICS 32500=GSBC 41902. Statistical Inference.
Sec 01: Timothy Conley, Mon, 1:30-4:30 PM, GSB to be announced.
PQ: Business 41901=STAT 32500
Required reading: DeGroot and Scherviah, Probability and Statistics. Lecture notes will be provided in the form of a CoursePack.

This Ph.D.-level course is the second in a two-quarter sequence with Business 41901. The central topic is statistical inference. The topics covered include Bayesian inference, classical estimation, decision theory, MCMC methods and Hierarchical models. The use of Hierarchical models is a focus in applications.

STATISTICS 32600=GSBC 37904=ECON 31601. Marketing Topics.
Sec 01: Peter Rossi, MW, 5:00-6:20 PM, GSB to be announced.

STATISTICS 33900=FINM 33100. Financial Data Analysis.
Sec 01: Staff, Mon, 6:30-9:30 PM, Ryerson 251.
PQ: Math Finance & Stat Students only; Crosslisted with STAT 33900.
Recommended textbooks:
Applied Linear Regression (3rd edition) by Sanford Weisberg (Wiley)
Time Series: Applications to Finance by Ngai Hang Chan (Wiley)
Statistical Analysis of Financial Data in S-Plus by Rene A. Carmona (Springer-Verlag)
Mathematical Statistics and Data Analysis by John A. Rice (Duxbury)
The Basics of S-plus, by Andreas Krause and Melvin Olson (Springer-Verlag)

Note that only part of each book will be used in the course. However, in the course of your career, you will find all of them useful to have on your shelf for further reading and reference.

STATISTICS 34500. Design and Analysis of Experiments.
Sec 01: Michael L. Stein, MW, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 34300
Required Reading: Mead, R., The Design of Experiments, Cambridge.
Recommended Reading: West, B. D., Welch, K. B. and Galecki, A. T., Linear Mixed Models., Chapman & Hall/CRC.

An introduction to the methodology and application of linear models in experimental design. A major focus of the course will be the basic principles of experimental design, such as blocking, randomization and incomplete layouts. Both standard designs, such as fractional factorials and incomplete block designs, as well as nonstandard designs, will be studied within this context. The analysis of these experiments will be developed as well, with particular emphasis on careful model formulation and the role of fixed and random effects. Time permitting, additional topics may include the use of covariates in the analysis of designed experiments, spatial analysis of field trials and Bayesian approaches to analysis of experimental data.

Course work will include the planning, execution and analysis of an experiment by the class.

STATISTICS 34920. Introduction to Nonparametric Regression.
Sec 01: David Degras, TTh, 10:30-11:50 AM, Room to be announced.
PQ: STAT 22400 or STAT 24500 or STAT 34300 or consent of instructor.
Required reading: Bowman, Adrian W. and Adelchi, Azzalini (1997). Applied Smoothing Techniques for Data Analysis, Oxford Science Publications.

This course provides an introduction to nonparametric regression models and methods. Theoretical and practical aspects will be presented, among which the construction of nonparametric estimators (kernel, spline, projection, local polynomial), the selection of smoothing parameters, some asymptotic theory (consistency, convergence rates, asymptotic distribution), and the construction of confidence regions. According to time left and to students' interests, further topics such as minimax estimation or adaptive estimation may be addressed. The methods will be implemented and illustrated in the R language environment.

STATISTICS 35700=HSTD 31001. Epidemiologic Methods.
Sec 01: Diane Lauderdale, TTh, 12:00-1:20 PM, BH W229.
PQ: HSTD 30900 or consent of instructor.
Required reading:

This course expands on the material presented in "Principles of Epidemiology," further exploring issues in the conduct of epidemiologic studies. The student will learn the application of both stratified and multivariate methods to the analysis of epidemiologic data. The final project will be to write the "specific aims" and "methods" sections of a research proposal on a topic of the student's choice.

STATISTICS 37000. Algebraic Methods in Statistics.
Sec 01: Mathias Drton, MWF, 10:30-11:20 AM, Eckhart 117.
PQ:
Required reading: Drton, M. Sturmfels, B., and Sullivant, S., Lectures on Algebraic Statistics.

Topics will include:

Markov bases for contingency table analysis
Likelihood ratio tests
Conditional independence models
Hidden variable models
Bayesian marginal likelihood integrals

STATISTICS 38300. Measure-Theoretic Probability-III.
Sec 01: Michael J. Wichura, MWF, 12:30-1:20 PM, Eckhart 117.
PQ: STAT 38100 or consent of instructor.
Required reading: No text book is required; notes will be distributed in class.

Topics for Stat 38300 will include:

The Hahn and Jordan decomposition theorems
Modes of convergence: with probability one, in probability, and in mean; uniform integrability
L2-spaces: projections; representation of linear functionals
The Radon-Nikodym theorem: absolute continuity, Radon-Nikodym derivatives; likelihood ratios; Lebesgue decompositions
Conditional probability: regular conditional probability distributions
Conditional expectation: given sub-sigma fields, and given measurable functions
Martingales: definitions and examples, transformations
Stopping times; optional sampling
Martingale limit and closure theorems
Backward submartingales
Continuous-time martingales: convergence, closure, optional sampling

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STATISTICS 47630. Monte Carlo Methods.
Sec 01: Steven P. Lalley, TTh, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 24600 or STAT 31200 or consent of instructor. Stat 47620 will meet the first 5 weeks of the term. Course runs first 5 weeks of quarter
Recommended reading: Liu, Jun, Monte Carlo Strategies in Scientific Computing. Papers to be announced later.

This will be a brief introduction to several useful techniques of simulation:

importance sampling and sequential importance sampling
MCMC (Markov chain Monte Carlo)
Gibbs sampling
perfect sampling

The utility of these methods will be illustrated by a number of substantial examples, including

self-avoiding random walks
enumeration of contingency tables with fixed margins
simulation of Ising models
code-breaking

STATISTICS 47900. Stochastic Models for Memory and Learning.
Sec 01: Yali Amit , TTh, 3:00-4:20 PM, Eckhart 117. Course begins 6th week.
PQ: Consent of instructor.
Required reading: None

This 5 week course will cover a some of the literature analyzing learning and memory in large neuronal populations as stochastic processes. First we will discuss models with discrete time dynamics, discrete binary neurons and finite state synapses, and derive bounds on memory capacity, learning and forgetting times. This will only involve discrete time Markov chain analysis and some ideas from mean-field analysis. Second we will introduce continuous integrate and fire neurons and continuous time dynamics. Using mean-field methods we will analyze the stability of large networks with random connections, and the behavior of the networks after learning. People interested in probability will be exposed to a rich collection of stochastic models waiting to be analyzed in a rigorous mathematical framework (the mean field analysis is only approximate.) People interested in neuroscience will be exposed to interesting models and some intriguing connections to experimental data.

AUTUMN 2008

College Courses

STATISTICS 20000. Elementary Statistics.
Sec 01: Keith Worsley, MWF 9:30-10:20 AM, Eckhart 133.
Sec 02: Phillip Lynch, MWF 12:30-1:20 PM, Eckhart 133.
PQ: Math 10600 or equivalent.
Required reading: Freedman, Pisani and Purves (2007). Statistics, 4th edition, Norton. ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. Not open to students with credit for STAT 22000 or 23400.

This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000=HDCP 22050. Stat Meth And Applications.
Sec 01: Han Xiao, MWF 10:30-11:20 AM, Eckhart 133.
Sec 02: Xiaoquan Wen, MWF 1:30-2:20 PM, Eckhart 133.
PQ: 2 QTRS Calculus.
Required reading: Moore, McCabe and Craig (2009). Introduction to the Practice of Statistics, 6th edition, W. H. Freeman. ISBN-10: 1429216220 ISBN-13: 978-1429216227

Students who matriculate in the College after September 2008 may count either STAT 22000 or STAT 23400, but not both, toward the forty-two credits required for graduation.

STATISTICS 22400=HSTD 32400. Applied Regression Analysis.
Sec 01: Vanja Dukic, TTh 10:30-11:50 AM, Eckhart 133
PQ: HSTD 32700 or STAT 22000 or STAT 23400 or STAT 24400 or consent of instructor.
Required reading: Chatterjee, Samprit and Hadi, Ali S., Regression Analysis by Example, 4th edition.

This course is an introduction to the methods and applications of fitting and interpreting multiple regression models. The main emphasis is on the method of least squares. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, the use of dummy variables, and tests of fit. The techniques discussed will be illustrated by many real examples involving biological and social science data. Examples and exercises will be implemented in a statistical software package "Stata", but familiarity with Stata is not required.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: David Degras, TTh, 3:00-4:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300, or equivalent.
Required Reading: Buntinas and Funk (2005). Statistics for the Sciences, Duxbury (Thomson Brooks/Cole). ISBN-10: 0534387748 ISBN-13: 978-0534387747.

Students who matriculate in the College after September 2008 may count either STAT 22000 or STAT 23400, but not both, toward the forty-two credits required for graduation.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

Stat 23400 takes the mathematical prerequisite quite seriously. Enrolling concurrently in either Math 13300, 15300, or 16300 while taking Stat 23400 is very strongly discouraged. Further, students who do not feel strong mathematically, may want to wait until completing their entire mathematical requirement (e.g., Math 19520-19620 for Economics majors) before enrolling in Stat 23400. Economics majors are strongly encouraged to delay taking Stat 23400 until the quarter just before enrolling in their required econometrics course (Econ 21000), for which Stat 23400 is a prerequisite. Thus, delaying Stat 23400 until at least late in the second year or even early in the third year of the Economics degree program should not be considered unusual.

STATISTICS 24400. Statistical Theory/Method-1.
Sec 01: Mathias Drton, TTH, 1:30-2:50 PM, Eckhart 133
Sec 02: Michael Stein, MW, 1:30-2:50 PM, Stuart 101
PQ: Multivariate Calculus (Math 19520 or 20000 or equivalent and Linear Algebra (Math 19620), 25500 or Stat 24300 or equivalent).
Required Reading: Rice, John A. (2007). Mathematical Statistics and Data Analysis, 3rd edition, Duxbury.

This is the first quarter of a two-quarter sequence. Enrollment in the first quarter alone is permitted, although not recommended. The first quarter will cover the essential tools from probability needed for study of statistical theory and the basic elements of statistical theory.

Topics will include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distribution, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions and chi-square tests. Some large sample theory will be included. The emphasis will be upon statistical theory, specifically upon concepts and tools that are useful for understanding and applying statistical methodology.

There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation.

The mathematics prerequisites are listed as general guidance. You should be comfortable with multivariate calculus through partial differentiation and multiple integration. Linear algebra is generally used only in 24500 and not 24400.

Graduate Courses

STATISTICS 30400. Distribution Theory.
Sec 01: Dan Nicolae, MWF, 1:30-2:20 PM, Eckhart 117
PQ: STAT 24500 or MATH 25000 or equivalent
Recommended reading: Severini, T. (2005). Elements of Distribution Theory, Cambridge University Press.

This course covers the basics of distribution theory. Topics include:

Distribution functions and their inverses, quantile functions, Q/Q plots, change-of-variables for probability densities
Expectation, variance, median, mode of random variables
Basics of measure theory, including Fubini's theorem and interchangeability of limits and integrals
Moment generating functions and characteristic functions, including power series expansion, inversion formulas, uniqueness theorems, and convergence in distribution
Cumulants and cumulant generating functions: examples, properties
Different concepts of convergence for random variables
Limit theorems, including the weak law of large numbers, the central limit theorem.

STATISTICS 30700=CMSC 37800. Numerical Computation.
Sec 01: Ronald Thisted, TTh, 9:00-10:20 AM, Eckhart 117
PQ: Linear Algebra (Stat 34300 or equivalent) and some previous experience with Statistics.
Required reading: Thisted, Ronald A. Elements of Statistical Computation, CRC/Chapman & Hall.
Recommended, but not required:

Gentle, James. Random Number Generation and Monte Carlo Methods, 2nd edition, Springer.
Watkins, David S. Fundamentals of Matrix Computations, 2nd edition, Wiley.
Scheinerman, Edward. C++ for Mathematicians. CRC/Chapman and Hall.

This course starts with a presentation of the fundamental algorithms for the solution of linear equations, the decomposition of matrices, and finite dimensional eigenvalue problems. Applications to least squares/regression will be presented, emphasizing use of existing numerical software. The course will also discuss optimization problems and introduce the basic principles of simulation-based methods.

Topics include:

Gaussian elimination and back-substitution
LU decomposition. (General/Symmetric)
Singular value decomposition. (Symmetric)
Householder orthogonalization and QR factorization. (Symmetric).
Iterative methods: Jacobi and Gauss Seidel.
Optimization: Newton-Raphson and quasi-Newton.
Uniform random number generation.
Simulating specific distributions
Monte Carlo methods

By the end of the course students should be able to apply these algorithms in their research work.

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STATISTICS 32300=HSTD 43000. Bayesian Methods and Computation.
Sec 01: Vanja Dukic, TTh, 3:00-4:20 PM, Eckhart 117.
PQ: STAT 301-302; 343; 312-313; OR consent of instructor.
Required reading: Tanner. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd edition, Springer.

This course will cover basics of modern statistical computation, with emphasis on Bayesian computational methods. It will begin with the introduction to Bayesian statistics, and cover normal and non-normal approximation to likelihood and posterior distributions, the EM algorithm, data augmentation and Markov Chain Monte Carlo (MCMC) methods. Time permitting, we will conclude with some recent developments in the MCMC area, such as perfect and adaptive sampling methods. Biostatistical and environmental examples will be given throughout the course. There will be weekly homeworks, and students will be expected to complete a project by the end of the course. There will be no final exam, but there will be an in-class final project presentation. Algorithms can be implemented in any language, but familiarity with R and Matlab will be assumed.

STATISTICS 33100. Sample Surveys.
Sect 01: Kirk Wolter, TTh, 10:30-11:50 AM, Eckhart 117
PQ: Consent of instructor
Required reading: Wolter, K.M. (2007). Introduction to Variance Estimation, 2nd edition, Springer-Verlag, New York.
Cochran, W.G. (1977). Sampling Techniques, 3rd edition, John Wiley & Sons, New York.

This is an introductory course to the statistics and methodology of sample surveys. Topics include

basic methods of sample selection,
determining sample size, stratification,
general estimators (Horvitz-Thompson, ratio, generalized regression, calibration)
domain estimation,
nonresponse,
nonsampling error,
multiple-stage sampling,
a national sampling frame for area probability surveys,
telephone surveys,
questionnaire design,
variance estimation for complex surveys,
analysis of contingency tables, and
regression analysis for survey data.

The course will be of interest to students who anticipate a research career that designs, collects, and analyzes survey data in fields such as economics, education, healthcare, marketing, psychology, sociology, and statistics.

STATISTICS 33500=GSBC 41910. Time-Series Analysis/Forecast.
Sec 01: Ruey-Shiong Tsay, Thu, 8:30-11:30 AM, GSB HC3B
PQ: Business 41901 or consent of instructor.
Required reading: No textbook. Some selected reference books are given below:

Hamilton, J. (1994). Time Series Analysis, Princeton University Press.
Tsay, Ruey S. (2005). Analysis of Financial Time Series, 2nd edition, Wiley.
A Course in Time Series Analysis, ed. Pena, Tiao and Tsay, Wiley, 2001.

GSB Honor Code: This course requires students to follow the GSB Honor Code and Standards of Scholarship in examinations and homework assignments. The GSB Honor Code requires students to sign the following pledge, "I pledge my honor that I have not violated the Honor Code during this examination," on every examination.

Course Ob jective:

To introduce time series analysis for econometric and financial applications
To discuss time series forecasting
To gain experience in model building
To assess the impacts of interventions and outliers
To understand state-space models and Kalman filter
To study MCMC methods and their applications in time series analysis
To discuss unit-root theory, trend-stationarity, testing and applications.

Lecture: Thursdays 8:30 to 11:30 am, starting September 25
Lecture Notes:
Outline of the lecture and some supplementary material will be posted on the web:
http://faculty.chicagogsb.edu/ruey.tsay/teaching/uts/.

STATISTICS 33610. Asymptotics for Time Series.
Sec 01: Wei Biao Wu, MW, 1:30-2:50 PM, Eckhart 117.
PQ: Business 30200 and STAT 31300 or consent of instructorf instructor.
Required reading:
Fan, J. and Yao, Q., (2003). Nonlinear time series, nonparametric and parametric methods. Springer, New York.
Wu, W. B. (2005): Nonlinear system theory: Another look at dependence. Proceedings of the National Academy of Sciences USA. 102, 14150--14154.
Wu, W. B. and Zhibiao Zhao (2007) Inference of Trends in Time Series. Journal of the Royal Statistical Society, Series B, 69, 391--410

I will present a systematic asymptotic theory for time series analysis. In particular, I will discuss asymptotics for sample mean, sample variances, banded covariance matrices estimates, inference of trends, periodograms, spectral density estimates, quantile estimation, nonparametric estimates, VaR and long-range dependent processes. Some asymptotic theory for non-stationary processes and functional linear models will also be presented.

STATISTICS 34300. Applied Linear Stat Methods.
Sec 01: Mathias Drton, TTh, 9:00-10:20 AM, Eckhart 133
PQ: STAT 24500 or equivalent and Linear Algebra (Stat 24300 or equivalent).
Optional Reading: Venables, W.N. and Ripley, B.D. (1999). Modern Applied Statistics with S-Plus (3rd ed). Springer-Verlag.
Required Reading: Weisberg, S. (2005). Applied Linear Regression, Third Edition. John Wiley & Sons. Software: Splus or R.

Statistics 34300 is an intensive course in the theory and methods of linear regression and related techniques of statistical modelling. It is intended primarily for graduate students in Statistics and related fields.

The course is also open to undergraduates and others who have a solid understanding of matrix algebra and basic statistical theory. Thorough familiarity with the simple linear regression model is expected.

The course will review linear regression with a single predictor, and will cover the multiple-predictor case; least-squares estimation; associated distribution theory; estimation, confidence intervals and tests; regression with errors in the predictors; weighted least squares, assessing lack of fit; residual analysis; regression diagnostics; transformations; model building; collinearity; subset-selection methods, including stepwise regression; prediction; nonlinear least squares.

STATISTICS 35000=HSTD 30900, ENST 27400, PPHA 36400. Principles of Epidemiology.
Sec 01: Kurina Lianne , MW, 10:30-11:50 AM, BSLC arr.
PQ: Introductory Statistics
Required reading:
Gordis, Leon. Epidemiology, 4th Ed. 2008. Saunders (available at the Barnes and Noble campus bookstore or at Amazon.com)
Original epidemiological articles (available on course website)

OBJECTIVES

To be able to critically read and understand epidemiologic studies.
To be able to calculate and interpret measures of disease occurrence and measures of disease-exposure associations.
To understand the contributions of epidemiology to clinical research, medicine, and public health.

STATISTICS 35201=HSTD 32901. Intro to Clinical Trials.
Sec 01: James Dignam, Tu, 3:00-5:50 PM, BH W230. Other ‘field trips’ as discussed below, will be recommended as additions or substitutes for class meetings.
PQ: Introductory statistics course (HSTD 32100, STAT 22000, or similar), ability to use a personal computer, or, permission of instructor.
Required reading: Piantadosi, S. Clinical Trials: A Methodologic Perspective, 2nd Edition 2005, Wiley Interscience, New York.
Reference: Friedman, L.M., Furberg, C.D., DeMets, D.L. Fundamentals of Clinical Trials., 3rd ed., 1998, Springer, New York.
Cook, T.D., DeMets, D.L. Introduction to Statistical Methods for Clinical Trials, 2007, Chapman & Hall/CRC, London.
Other readings: Selected articles for class discussion will be identified and distributed the prior week. A reading list of these methodological articles, commentaries, and clinical trial report articles will be developed and distributed.

This course will review major components of clinical trial conduct, including the formulation of clinical hypotheses and study endpoints, trial design, development of the research protocol, trial progress monitoring, analysis, and the summary and reporting of results. Other aspects of clinical trials to be discussed include ethical and regulatory issues in human sub jects research, data quality control, meta-analytic overviews and consensus in treatment strategy resulting from clinical trials, and the broader impact of clinical trials on public health.

The course will be conducted partly via lectures and partly in a ‘reading’ format. Designated individuals may take the lead in covering a main topic, with participation and input by all. Similarly, presentation of materials in the special topics portion of the class meeting will be shared among all.

STATISTICS 35581. Statistical Methods and Gene Networks.
Sec 01: Matthew Stephens, TTh, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 24400 or consent of instructor.
Required reading: No textbook required.

This is an advanced graduate seminar-style course in statistical genetics. We will read and critique published papers that use statistical methods to infer gene networks. We expect to cover a range of different types of data types (eg gene expression data, CHIP-chip data, literature mining) and statistical approaches (eg Bayes Networks, similarity measures), with an emphasis on understanding the details of the statistical approach used. Students will be expected to be familiar and facile with a range of statistical concepts, including statistical modelling, maximum likelihood and Bayesian inference, and hypothesis testing. Students will be expected to read papers in advance of each class, and to take turns in presenting and leading the discussion.

STATISTICS 36800. Wavelets in Statistics.
Sec 01: Debashis Mondal, TTh, 12:00-1:20 PM, Eckhart 117.
PQ: STAT 30200 or consent of instructor.
Required reading: None
Recommended reading: Vidakovic, Brani (1999). Statistical Modeling by Wavelets, Wiley, ISBN 0471293652, 9780471293651.

This course introduces wavelet methods in statistics. Topics include

wavelet transform, shrinkage estimators, non-parametric regression,
density estimation, deconvolution, inverse problems, and
selected applications in time series analysis.

STATISTICS 36900=HSTD 33300. Longitudinal Data Analysis.
Sec 01: Paul Rathouz, TTh, 9:00-10:20 AM, BSLC 313.
PQ: Statistics 220 (introductory statistical methods), Health Studies 321 or equivalent, Health Studies 324 / Statistics 224 (Applied Regression Analysis) or equivalent, and Health Studies 327 / Statistics 227 (Biostatistical Methods) or equivalent; or permission of instructor. Facility with the Stata software package is assumed.
Required reading: (Available in bookstore.)
* Fitzmaurice GM, Laird NM, Ware JH. (2004). Applied Longitudinal Analysis. Hoboken, NJ: Wiley-Interscience.

Other references: (* Indicates text on reserve in Eckhart Library.)
* Diggle PJ, Heagerty P, Liang K-Y, & Zeger SL. (2002). Analysis of Longitudinal Data, 2nd edn. Oxford: Oxford University Press.
* Hedeker DR & Gibbons RD. (2006). Longitudinal data analysis. Hoboken, NJ: Wiley-Interscience. Hand D, Crowder M. (1996). Practical Longitudinal Data Analysis. London: Chapman & Hall.
Lindsey JK. (1999). Models for repeated measurements. New York: Oxford University Press.
Littel RC, Milliken GA, Stroup WA, Wolfinger RD. (1996). SAS System for Mixed Models. Cary, NC: SAS Institute.
* McCulloch CE, Searle SR. (2001). Generalized, Linear, and Mixed Models. New York: John Wiley & Sons.
Verbeke G, Molenberghs G. (2000). Linear mixed models for longitudinal data. New York: Springer.

Longitudinal data consist of multiple measures over time on a sample of individuals. This type of data occurs extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Emphasis will be on data analysis and interpretation. Supporting statistical theory will be given at a level appropriate for an advanced Master’s student in Statistics. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies.

STATISTICS 37910=CMSC 35510. Statistical Methods in Computer Vision.
Sec 01: Yali Amit, TTh, 10:30-11:50 AM, Ryerson 277.
PQ: Consent of instructor.
Required reading: Instructor’s notes and selected papers.

The first part of this course will provide an introduction to pattern recognition, and an overview of statistical modeling of high dimensional data and parameter estimation methods. Sub jects include graphical models, mixture models, Maximum likelihood and Bayesian estimation, the EM algorithm and its variants. These methods will then be employed in a number of computer vision algorithms involving ob ject detection and recognition. Class work will consist of some theoretical assignments and implementation of some of the algorithms in code on real data. In addition we will present a brief introduction to C++ and provide the students with a set of tools to facilitate the programming assignments.

STATISTICS 38100. Measure-Theoretic Probability I.
Sec 01: Michael Wichura, MWF, 2:30-3:20 PM, Eckhart 117.
PQ: STAT 31300 or consent of instructor.
Required Reading: There is no text required or recommended. Notes will be provided.

This course is the first of a three quarter sequence presenting a careful development of some topics from measure and probability. Topics to be covered in 381 include: classes of sets -- fields, sigmafields, monotone classes, pi and lambda systems; probabilities and general measures; independence and the Borel-Cantelli lemmas; measurable functions; induced measures, distribution and inverse distribution functions; integration with respect to measures -- basic properties, change of variable, indefinite integration, densities; integration to the limit -- MCT, DCT, and friends; laws of large numbers, applications to probability and statistics; transition probabilities and product measures.

STATISTICS 39000=FINM 34500. Stochastic Calculus I.
Sec 01: Per Mykland, M, 6:30-9:30 PM, Ryerson 251.
PQ: Enrollment in Mathematical Finance M. Sc. program or consent of instructor.
Required Reading: Stochastic Calculus for Finance I-II, by Steven E. Shreve (required).
A Course in Financial Calculus, by Alison Etheridge (highly recommended).
`The basics of S-PLUS'', 3rd edition, by the same authors (A. Krause and M. Olson), ISBN 0-387-95456-2 (required).

This course is an introduction to stochastic calculus as it is relevant to the pricing and hedging of options and other derivative securities. It is the first of a two-quarter sequence offered in collaboration by the Department of Statistics and the master's program in Mathematical Finance. The main topics to be covered are:

The Fundamental Theorem of Asset Pricing
Martingales
Brownian Motion
The Ito Integral and Ito's Formula
The Black-Sholes Formula
Girsanov's Formula
Currency Options
The Martingale Representation and Hedging

There will be weekly homework assignments, and midterm and final exams. The course assistants will conduct weekly help sessions on Friday afternoons.

STATISTICS 47620. Simulation Methods.
Sec 01: Steven P. Lalley, MWF, 10:30-11:20 AM, Eckhart 117
PQ: STAT 24600 or STAT 31200 or consent of instructor. Stat 47620 will be taught starting October 27th, 2008 and credits will be 50.
Required Reading: Monte Carlo Strategies in Scientific Computing by Jun Liu.

This will be a brief introduction to several useful techniques of simulation:

importance sampling
MCMC (Markov chain Monte Carlo)
Gibbs sampling
perfect sampling

The utility of these methods will be illustrated by a number of substantial examples, including

enumeration of contingency tables with fixed margins
simulation of Ising models
code-breaking.

SPRING 2008

College Courses

STATISTICS 20000. Elementary Statistics.
Sec 01: Dan Wang, MWF 9:30-10:20 AM, Eckhart 133.
Sec 02: Wenlong Wang, MWF 12:30-1:20 PM, Eckhart 133.
PQ: Math 10500 or equivalent.
Required reading: Statistics, 4th edition, by Freedman, Pisani, Purves 2007, Norton.
ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000. Statistical Methods and Their Applications.
Sec 01: Zuoheng Wang, MWF 10:30-11:20 AM, Harper Memorial 140.
Sec 02: Shali Wu, MWF 1:30-2:20 PM, Eckhart 133.
PQ: 2 QTRS Calculus.
Required reading: Introduction to the Practice of Statistics, 5th edition by Moore and McCabe
2006, W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007

STATISTICS 22200 Linear Models and Experimental.
Sec 01: Linda Collins, TTh, 9:00-10:20 AM, Eckhart 133.
PQ: STAT 22000 or consent of instructor.
Required Reading: Oehlert, G. W. (2000) A First Course in Design and Analysis of Experiments. W. H. Freeman. ISBN-10: 0-7167-3510-5 ISBN-13: 978-0-7167-3510-6.

In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment of their own.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Pending, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Michael Finegold, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300.
Required Reading:

Investigating Statistical Concepts, Applications, and Methods by Chance and Rossman 2006, Duxbury (Thomson Brooks/Cole). ISBN-10: 0495050644, ISBN-13: 978-D495050643.

A Brief Course in Mathematical Statistics by Tanis and Hogg 2007, Prentice Hall, ISBN-10: 0131751395, ISBN-13: 978-0131751392.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24500. Statistical Theory and Methods 2.
Sec 01: Debashis Mondal, TTh, 1:30 PM, Eckhart 133.
PQ: STAT 23400 and STAT 23500 or STAT 24400 or consent of instructor.
Required reading: Mathematical Statistics and Data Analysis, 3rd edition, Rice, John A. 2007, Duxbury. ISBN-10: 0534399428, ISBN-13: 978-0534399429.

STATISTICS 24600. Statistical Theory and Methods 3.
Sec 01: Yali Amit, TTh, 10:30-11:50 AM, Eckhart 133.
PQ: STAT 24400 and STAT 24500 or consent of instructor.
Required Reading: Pattern Recognition and Machine Learning, Bishop, Christopher M. 2006, Springer Verlag. ISBN-10: 0387310738, ISBN-13: 978-0387310732.

This course will introduce a variety of modern statistical methods. We will start with the description of families of multivariate models - multivariate normal distribution, graphical models, log-linear models. We will then discuss inference for such models including the EM algorithm, Bayesian inference and Monte-carlo methods. The main application of the models will be in classification problems - i.e the 'generative approach'. We will also introduce some modern discriminative approaches for classification.

STATISTICS 25100. Intro to Math Probability.
Sec 01: Michael J. Wichura, TTh, 12:00-1:20 PM, Eckhart 312.
PQ: MATH 20000 or MATH 20400 or consent of instructor.
Required Reading: A First Course in Probability, 7th edition, Ross, S. 2005, Pearson/Prentice Hall. ISBN-10: 0131856626, ISBN-13: 978-0131856622.

For more information, please visit http://www.stat.uchicago.edu/~wichura/Stat251/courseinfo.html

STATISTICS 26100=STATISTICS 33600. Time Dependent Data.
Sec 01: MIchael Stein, TTH, 1:30-2:50 PM, Eckhart 202.
PQ: STAT 24400 or STAT 24500.
Required Reading: Time Series Analysis and Its Applications: With R Examples, 2nd edition
Shumway and Stoffer 2006, Springer. ISBN-10: 0387293175, ISBN-13: 978-0387293172.

This course considers the modeling and analysis of data that are ordered in time. The main focus will be on quantitative observations taken at evenly spaced intervals and will include both time-domain and spectral approaches. Time permitting, statistical approaches to other data types, such as categorical observations or point processes, will be considered.

STATISTICS 26700=CHSS 32900, HIPS 25600, STAT 36700.
Sec 01: Stephen Stigler, MWF, 9:30-10:20 AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: The History of Statistics: The Measurement of Uncertainty Before 1900.
Stigler, Stephen M. 1990, Belknap Press of Harvard University Press. ISBN-10: 067440341X,
ISBN-13: 978-0674403413. Other materials will be distributed in class or by web.

Graduate Courses

STATISTICS 30200. Mathematical Statistics 2.
Sec 01: Wei Biao Wu, MW, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30100 or consent of instructor.
Required Reading: Mathematical Statistics, 2nd ed. 2003. Corr. 4th printing edition (October 5, 2007). Jun Shao, Springer. ISBN-10:0387953825, ISBN-13: 978-0387953823.

This course continues the development of mathematical statistics. Topics of importance include: statistical decision theory, admissability and the Neyman-Pearson lemma, formal hypothesis testing, comparison with conditional frequentist and Bayesian viewpoints, ancillarity, interval estimation, UMP tests and MLR, unbiased tests, score statistics, generalized likelihood ratio tests and asymptotics, minimax estimation, empirical Bayes and "shrinkage".

STATISTICS 31300. Introduction to Stochastic Processes 2.
Sec 01: Per A. Mykland, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: STAT 31200 or consent of instructor.
Required reading:

STATISTICS 32200. Bayesian Data Analysis.
Sec 01: Matthew Stephens, MW, 1:30-2:50 PM, Eckhart 308.
PQ: Consent of instructor.
Reading: There is no required text, but "Bayesian Theory" by Bernardo and Smith is background reading.

This course is aimed at graduate students in statistics, and others with the necessary statistical background. We will assume familiarity with standard statistical distributions (e.g. Normal, Poisson, Binomial, Exponential), with the laws of probability, and concepts of statistical inference (maximum likelihood estimation, hypothesis testing, confidence intervals, etc), and basic familiarity with the R statistical package.

The course will cover foundations of Bayesian statistics, including axiomatic development, exchangeability, De Finetti's theorem, Jeffreys and improper priors, decision theory, Bayesian hypothesis testing and Bayes factors. Concepts will be illustrated mainly by instructive "toy" examples, where calculations can be done by hand. However, we will also study more complex, practical applications of Bayesian statistics. Although methods of computation will be discussed, the primary focus will be on concepts, and not on computation.

STATISTICS 33200=HSTD 43200. Causal Inference.
Sec 01: Tyler Vanderweele, TTH, 10:30-11:50 AM, BSLC arr.
PQ: STAT 22400-22600 or HSTD 32400-32700 or equivalent or consent of instructor.
Required reading:

STATISTICS 34700. Generalized Linear Models.
Sec 01: Peter McCullagh, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 34300 or consent of instructor.
Required Reading: Generalized Linear Models, 2nd edition, McCullagh and Nelder 1990, Chapman & Hall/CRC. ISBN-10: 0412317605, ISBN-13: 978-0412317606.
Recommended Reading: Modern Applied Statistics with S, 4th edition, Venables and Ripley 2003, Springer. ISBN-10: 0387954570, ISBN-13: 978-0387954578.
Applied Statistics: Principles and Examples, Cox and Snell 19821 Chapman & Hall/CRC. ISBN-10: 0412165708, ISBN-13: 978-0412165702.

This is an applied course for students who are familiar with linear models at the level of Draper and Smith or Weisberg. The following topics will be covered:

Factors, variates, contrasts, interactions
Exponential-family models: variance function
Definition of a generalized linear model: link functions
Analysis of deviance
Specific examples of GLMs

logistic and probit regression
cumulative logistic models
log-linear models and contingency tables
inverse linear models

Quasi-likelihood and least squares; estimating functions
Over-dispersion
Partially linear models

STATISTICS 35500. Statistical Genetics.
Sec 01: Mary Sara McPeek, F, 1:30-4:10 PM, Eckhart 117.
PQ: Human Genetics 471 and Statistics 244 and 245. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor.
Reading: There is no textbook.

This is an advanced course in statistical genetics. Prerequisites are Human Genetics 471 and Statistics 244 and 245. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor. This is a discussion course and student presentations will be required. Topics vary and may include, but are not limited to, statistical problems in association mapping, linkage mapping, population genetics, microarray analysis, and genetic models for complex traits.

STATISTICS 36700=CHSS 32900, HIPS 25600, STAT 26700.
Sec 01: Stephen Stigler, MWF, 9:30-10:20 AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: Stephen M. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900. (Cambridge, Mass.: Harvard University Press, 1986.) (Available in paperback.) Other materials will be distributed in class or by web.

STATISTICS 38600. Topics in Stochastic Processes.
Sec 01: Steven P. Lalley, TTH, 3:00-4:20 PM, Eckhart 117.
PQ: Consent of instructor.
Recommended Reading: Stochastic Differential Equations by Oksendal (PG)
Brownian Motion and Stochastic Calculus by Karatzas and Shreve (R)
Foundations of Modern Probability by Kallenberg (X)

Topics:

Wiener process (Brownian motion)
Weak Convergence in Function Space
Ito Integral and Ito Calculus
Introduction to Diffusion Processes

STATISTICS 43800. Statistical Inference for Financial Data.
Sec 01: Per A. Mykland, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30200, 34300 and 38300 or consent of instructor.
Required Reading:

WINTER 2008

HSTD 43501. Theory and Methods for Multivariate and Longitudinal Data.
Sec 01: Paul Rathouz and Tyler VanderWeele, MW 1:30-2:50 pm, BSLC Arr.
PQ: Statistics 304, 301, 302, 343, 347. Statistics Statistics 244, 245, 246 with experience or coursework in matrix linear algebra may be substituted for Statistics 304, 301, 302.
Required reading: Instructor’s notes and selected papers.

This course presents a theoretical treatment of methods for multivariate and longitudinal data. The course covers both continuous and categorical data. Focus will be primarily on likelihood-based methods and their direct extensions. The first two-thirds of the course will cover mean and covariance models for multivariate normal data. Applications and special cases will include Hotelling's T-test,
multivariate linear regression, linear mixed and growth curve models, linear structural equations models and graphical models. The last one-third of the course will focus on categorical outcomes and will include generalized linear mixed models, structural equations models for categorical data and generalized linear marginal models. Readings will be taken from selected texts and original articles in the statistical literature. Students should expect four homework sets focused on theory and programming tasks related to methods developed in the course, as well as a final programming project.

Topic List (number of lectures) [expected instructor]
----------
(1) Review of Theory for Multivariate Normal Distribution [tv]
(1) Wishart Distribution and Hotelling's T-test [tv]
(4) General Linear Model for Correlated Data [pr]

Models for the mean and variance-covariance of multivariate processes
Likelihood methods for multivariate linear regression models
Hypothesis tests for variance-covariance parameters in non-standard situations

(7) Applications and Special Cases of the General Linear Model for Correlated Data [tv]

Linear Mixed Models and Growth Curve Models
Structural Equations Models
Multivariate Normal Graphical Models

(6) Generalized Linear Mixed Models -- Likelihood Methods [pr]

Canonical link models and the EM algorithm
Numerical integration techniques
MC integration

(time permitting) Structural Equations Models for Categorical Data [tv]

(time permitting) Generalized linear marginal models for categorical data (see Section 8.2 in DHLZ, Zhao and Prentice, Heagerty) [pr]

College Courses

STATISTICS 20000. Elementary Statistics.
Sec 01: Wei Biao Wu, MWF 9:30-10:20 AM, Eckhart 133.
PQ: Math 10600 or equivalent.
Required reading: Freedman, Pisani, and Purves, Statistics, 4th edition. W. W. Norton Press. ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000. Statistical Methods and Their Applications.
Sec 01: Debashis Mondal, MWF 10:30-11:20 AM, Eckhart 133.
PQ: 2 QTRS Calculus.
Required reading: Moore and McCabe, Introduction to the Practice of Statistics, 5th edition. W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007.

STATISTICS 22600=HSTD 32600. Analysis of Categorical Data.
Sec 01: Mei Wang, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 22000 or equivalent.
Required reading: Agresti, A. An introduction to Categorical Data Analysis. Wiley, 2nd ed., 2007.

STATISTICS 22700=HSTD32700. Biostatistical Methods.
Sec 01: Ronald A. Thisted, TTH, 10:30-11:50 AM, BSLC 202.
PQ: HSTD 32400/STAT 22400 or STAT 24500 or equivalent; or consent of instructor.
Required reading: Collett, D. (2003). Modelling Binary Data, Second Edition. Boca Raton, Chapman & Hall/CRC.
Collett, D. (2004). Modelling Survival Data in Medical Research, Second Edition. London, Chapman & Hall.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Nina Singhal Hinrichs, MWF, 2:30-3:20 PM, Ryerson 276 (as of 1/9/08).
PQ: Mathematics 13300, 15300 or 16300.
Required Reading: Chance and Rossman (2005). Investigating Statistical Concepts, Applications, and Methods, First Edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.
Tanis and Hogg (2008). A Brief Course in Mathematical Statistics, Pearson/Prentice Hall, ISBN: 0-1317-5139-5.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24400. Statistical Theory and Methods I.
Sec 01: Mathias Drton, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: MATH 19600, 20100, or 20500.
Required reading: Rice, John A. (2007). Mathematical Statistics and Data Analysis, Third Edition, by (Duxbury).

There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited and brief, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation. Statistical software will be used for simulations and data analysis.

STATISTICS 24500. Statistical Theory and Methods II.
Sec 01: Wei Biao Wu, TTH, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 24400 or consent of instructor.
Required reading: Rice, J. A. (2006). Mathematical Statistics and Data Analysis, 3rd ed., Brooks/Cole.

STATISTICS 24700=CPNS 32100. Math/Stats Methods for Neuroscience-2.
Sec 01: William Van Drongelen, WF, 1:30-2:50 PM, BSLC 401.
PQ: STAT 24400 or consent of instructor.
Required reading: Rice, J. A. (1995). Mathematical Statistics and Data Analysis, 2nd ed., Duxbury.

This course deals with the application of non-linear methods in signal processing and dynamical systems theory to issues in neuroscience. Data analysis with Matlab is again emphasized.

The third course in this sequence is an elective course in one of the quantitative sciences relevant to neuroscience that can be selected by the student in consultation with the program chair.

STATISTICS 25200=STAT 31200. Introduction to Stochastic Processes I.
Sec 01: Steven P. Lalley, TTH, 10:30-11:50 AM, Ryerson 277 .
PQ: STAT 25100 or consent of instructor
Required reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

Graduate Courses

STATISTICS 30100. Mathematical Statistics I.
Sec 01: Mary Sara McPeek, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30400 and MATH 20500 or consent of instructor.
Required Reading: Casella and Berger. Statistical Inference, 2nd ed.
Ferguson. A Course in Large Sample Theory.

STATISTICS 30600. Adv. Statistical Inference 1.
Sec 01: Peter McCullagh, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: Consent of instructor.
Suggested reading: Tensor Methods in Statistics by P. McCullagh.
Principles of Statistical Inference by L. Pace and A. Salvan.
Statistical Models by A.C. Davison.

The focus of the course will be on parametric statistical models and likelihood.

Topics for discussion include

Statistical models: definition by example
Likelihood and likelihood ratio statistic
Asymptotic approximation of distributions
Edgeworth and related approximations
Cumulant calculations and tensor calculus
Marginal likelihood and REML estimation
Projection and Best linear prediction: splines
Processes, exchangeable processes, modulated processes, regression processes
Bayesian models

STATISTICS 31200=STAT 25200. Introduction to Stochastic Processes 1.
Sec 01: Steven P. Lalley, TTH, 10:30-11:50 AM, Ryerson 277.
PQ: STAT 25100 or consent of instructor.
Required Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

STATISTICS 31700=STAT 25300. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 133.
PQ: STAT 25100 or STAT 24400 or equivalent. Consent of instructor.
Required reading: Ross, R., Introduction to Probability Models, 9th ed., (2007).

STATISTICS 32500=GSBC 41902. Statistical Inference.
Sec 01: Nicholas G. Polson, Tue 8:30-11:30 AM, GSB C10.
PQ: Business 41901=STAT 32500
Required reading: DeGroot and Scherviah, Probability and Statistics. Lecture notes will be provided in the form of a CoursePack.

STATISTICS 34500. Design and Analysis of Experiments.
Sec 01: Michael L. Stein, MW, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 34300
Reading: Mead, R., The Design of Experiments. Cambridge.
West, B.D., Welch, K.B. and Galecki, A.T., Linear Mixed Models. Chapman & Hall/CRC.

Course work will include the planning, execution and analysis of an experiment by the class.

STATISTICS 35540. Population Genetics and Metagenomics.
Sec 01: Nicholas Eriksson, MW, 1:30-2:50 PM, Eckhart 117.
Required reading: Selected papers.

In this course we will focus on the biological, statistical, and computational challenges involved in the analysis of metagenomic data. In metagenomics and population genetics, the goal is to understand the patterns of variation in genomes within species and between related species. New sequencing technologies allow us to gather data on unexplored populations, but require new methods of analysis. We will look at the connections between these problems and more established fields such as phylogenetics, sequence assembly, and population genetics.

Topics will be chosen from recent papers from the literature; some of these papers will be presented by seminar participants.

STATISTICS 35600=HSTD 33100. Applied Survival Analysis.
Sec 01: James Dignam, TTH, 10:30-11:50 AM, BSLC 305.
PQ: HSTD 32100; STAT 22000; or equivalent, and HSTD 32400/STAT 22400 or equivalent; or consent of instructor.
Required reading:

This course will provide an introduction to the principles and methods for the analysis of time-to-event data. This type of data occurs extensively in both observational and experimental biomedical and public health studies, as well as in industrial applications. While some theoretical statistical detail is given (at the level appropriate for a Master's student in statistics), the primary focus will be on data analysis. Problems will be motivated from an epidemiologic and clinical perspective, concentrating on the analysis of cohort data and time-to-event data from controlled clinical trials.

STATISTICS 35700=HSTD 31001. Epidemiologic Methods.
Sec 01: Diane Lauderdale, TTH, 12:00-1:20 PM, BH W229.
PQ: HSTD 30900 or consent of instructor.
Required reading:

STATISTICS 37300. Graphical Models and Algebraic Statistics.
Sec 01: Mathias Drton, TTH, 1:30-2:50 PM, Eckhart 202.
PQ: Consent of instructor.
Required reading: None.

In graphical modelling, one associates a statistical model with a graph: nodes represent variables and edges indicate dependencies between variables. This framework is popular in many applied areas and encompasses models such as Bayesian networks, log-linear models, phylogenetic tree models, multivariate regression, factor analysis, and structural equation models. Topics to be discussed in this course include conditional independence, directed and undirected graphical models, hidden variables, and statistical inference in the different model classes. We will also discuss algebraic techniques that are useful in the study of graphical models.

STATISTICS 38300. Measure-Theoretic Probability-3.
Sec 01: Michael J. Wichura, MWF, 12:30-1:20 PM, Eckhart 117.
PQ: STAT 38100 or consent of instructor.
Required reading: No text book is required; notes will be distributed in class.

Topics for Stat 38300 will include:

The Hahn and Jordan decomposition theorems
Modes of convergence: with probability one, in probability, and in mean; uniform integrability
L2-spaces: projections; representation of linear functionals
The Radon-Nikodym theorem: absolute continuity, Radon-Nikodym derivatives; likelihood ratios; Lebesgue decompositions
Conditional probability: regular conditional probability distributions
Conditional expectation: given sub-sigma fields, and given measurable functions
Martingales: definitions and examples, transformations
Stopping times; optional sampling
Martingale limit and closure theorems
Backward submartingales
Continuous-time martingales: convergence, closure, optional sampling

Back to top

STATISTICS 39000=FINM 34500. Stochastic Calculus-1.
Sec 01: Jostein Paulsen, W, 6:00-9:00 PM, Ryerson 251.
PQ: Math Finance Students Only. The course, Mathematical Foundations of Option Pricing is very useful. Otherwise, the more mathematical analysis you know, the better.
Required Reading: Tomas Bjork: Arbitrage Theory in Continuous Time. Oxford University Press
Steven Shreve: Stochastic Calculus for Finance II: Continuous-Time Models. Springer Finance.

In the class we will give a more or less in depth coverage of the following topics. Notes are provided by the instructor.

• Basic stochastic calculus including martingales, filtrations, Brownian motion, the
Itô integral, Itô's formula and stochastic differential equations.

Option pricing, both with deterministic interest rate and with stochastic interest rate. Also option hedging.
Forwards and futures
Foreign exchange derivative pricing
Interest rate models including classical models, the Heath-Jarrow-Morton approach and the Market Models.
Some credit risk models

STATISTICS 47900. Stochastic Models for Memory and Learning.
Sec 01: Yali Amit , TTH, 3:00-4:20 PM, Eckhart 117. Course begins 6th week.
PQ: Consent of instructor.
Required reading: None

STATISTICS 48400. Adv. Topics in Probability 1.
Sec 01: Steven P. Lalley, TTH, 3:00-4:20 PM, Eckhart 117. Course held 1st-5th weeks of quarter.
PQ: Basic probability and linear algebra, some elementary complex variable theory.
Recommended reading:

"Methodologies in spectral analysis of large dimensional random matrices, a review" by Z. D. Bai , Statistica Sinica 9 (1999), 611-677.
"High dimensional statistical inference and random matrices" by I. Johnstone, http://front.math.ucdavis.edu/0611.5589.
"Introduction to the Random Matrix Theory: Gaussian Unitary Ensemble and Beyond" by Yan V. Fyodorov, http://front.math.ucdavis.edu/0412.4717.
"Orthogonal Polynomials and Random Matrices: A Riemann-Hilbert Approach" by P. Deift (mainly ch. 5), http://www.ams.org/bookstore.

This 5-week course will introduce the basic theory of large random matrices. The first part of the course will be devoted to "bulk spectral" properties of Wigner and sample covariance matrices
(that is, the empirical distribution of their eigenvalues), leading to the Wigner semi-circle law and the Marchenko-Pastur theorem. The second part will focus on the top of the spectrum, and will (I hope) give a mostly complete derivation of the "Tracy-Widom" distribution.

AUTUMN 2007

College Courses

STATISTICS 20000. Elementary Statistics.
Sec 01: Xinghua Zheng, MWF 9:30-10:20 AM, Eckhart 133
Sec 02: Minsun Song, MWF 12:30-1:20 PM, Eckhart 133
PQ: Math 10600 or equivalent.
Required reading: Freedman, Pisani, and Purves, Statistics, 4th edition. W. W. Norton Press. ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

STATISTICS 22000. Statistical Methods and Their Applications.
Sec 01: Shali Wu, MWF 10:30-11:20 AM, Eckhart 133
Sec 02: Marcin Hitczenko, MWF 1:30-2:20 PM, Eckhart 133
PQ: 2 QTRS Calculus.
Required reading: Moore and McCabe, Introduction to the Practice of Statistics, 5th edition. W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007.

STATISTICS 22400=HSTD 32400. Applied Regression Analysis.
Sec 01: Vanja Dukic, TTH 10:30-11:50 AM, Eckhart 133
PQ: HSTD 32700 or STAT 22000 or STAT 23400 or STAT 24400 or consent of instructor.
Required reading:

STATISTICS 23400. Statistical Models/Method-1.
Sec 01: Linda Collins, MWF 11:30-12:20 PM, Eckhart 133
Sec 02: Nicholas Eriksson, MWF 2:30-3:20 PM, Eckhart 133
PQ: MATH 13300, 15300 or 16300
Required reading: Chance and Rossman, Investigating Statistical Concepts, Applications, and Methods. Thompson, Brooks/Cole, ISBN-10: 0495050644, ISBN-13: 978-0495050643.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24400. Statistical Theory/Method-1.
Sec 01: Michael Stein, TTH, 1:30-2:50 PM, Eckhart 133
PQ: MATH 19600, 20100, or 20500
Required Reading: Rice, John A. (2007). Mathematical Statistics and Data Analysis, Third Edition, by (Duxbury).

The mathematics prerequisites are listed as general guidance. You should be comfortable with multivariate calculus through partial differentiation and multiple integration.

Graduate Courses

STATISTICS 30400. Distribution Theory.
Sec 01: Dan Nicolae, MWF, 1:30-2:20 PM, Eckhart 117
PQ: STAT 24500 or MATH 25000 or equivalent
Recommended reading: Severini, T. (2005). Elements of Distribution Theory. Cambridge University Press.

This course covers the basics of distribution theory. Topics include:

Distribution functions and their inverses, quantile functions, Q/Q plots, change-of-variables for probability densities
Expectation, variance, median, mode of random variables
Basics of measure theory, including Fubini's theorem and interchangeability of limits and integrals
Moment generating functions and characteristic functions, including power series expansion, inversion formulas, uniqueness theorems, and convergence in distribution
Cumulants and cumulant generating functions: examples, properties
Different concepts of convergence for random variables
Limit theorems, including the weak law of large numbers, the central limit theorem.

STATISTICS 30700=CMSC 37800. Numerical Computation.
Sec 01: Ronald Thisted, TTH, 10:30-11:50 AM, Ryerson 276
PQ: Stat 34300 (concurrent enrollment OK) or consent of instructor.
Required reading: Thisted, Ronald A. Elements of Statistical Computation. CRC/Chapman & Hall.
Recommended, but not required:
   Gentle, James. Random Number Generation and Monte Carlo Methods. Second edition.
   Springer.
   Watkins, David S. Fundamentals of Matrix Computations. Second edition.
   Wiley.
   Scheinerman, Edward. C++ for Mathematicians. CRC/Chapman and Hall.

Topics include:

Gaussian elimination and back-substitution
LU decomposition. (General/Symmetric)
Singular value decomposition. (Symmetric)
Householder orthogonalization and QR factorization. (Symmetric).
Iterative methods: Jacobi and Gauss Seidel.
Optimization: Newton-Raphson and quasi-Newton.
Uniform random number generation.
Simulating specific distributions
Monte Carlo methods

By the end of the course students should be able to apply these algorithms in their research work.

STATISTICS 32301=HSTD 43001. Advanced Bayesian Methods.
Sec 01: Vanja Dukic, W, 12:30-3:20 PM, BH W230
PQ: STAT 32300/HSTD 43000 or STAT 30100-30200, 31200-31300 and consent of instructor
Required reading: Notes and manuscripts will be distributed in class.

This class is a continuation of the Bayesian Topics (Stat 32300/HSTD 43000). We will move beyond the material learned there (the basics of Bayesian statistics and computation (importance sampling, EM, MCEM, data augmentation, Metropolis-Hastings and Gibbs sampling). In particular, we will focus on extensions to MCMC geared for dealing with high-dimensional problems with potential multimodality (simulated tempering, sequential Monte Carlo, Hamiltonian MCMC, Langevin MCMC). We will also discuss issues and algorithms for model comparison (transdimensional MCMC and algorithms for computation of normalizing constants). Algorithms can be implemented in any language, but familiarity with R or Matlab will be assumed. The class will have a seminar format.

STATISTICS 32400=GSBC 41901. Probability and Statistics.
Sec 01: Nicholas Polson, Tue, 8:30-11:30 AM, HC3B
PQ: One year of Calculus
Required reading: The text for the course is DeGroot and Schervish, Probability and Statistics. Lecture notes will be available in the form of a CoursePack.

STATISTICS 33100. Sample Surveys.
Sect 01: Kirk Wolter, TTH, 10:30-11:50 AM, Eckhart 117
PQ: Consent of instructor
Required reading: Wolter, K.M. (2007). Introduction to Variance Estimation, 2nd Edition, Springer-Verlag, New York.

This is an introductory course to the statistics and methodology of sample surveys. Topics include

basic methods of sample selection,
determining sample size, stratification,
general estimators (Horvitz-Thompson, ratio, generalized regression, calibration)
domain estimation,
nonresponse,
nonsampling error,
multiple-stage sampling,
a national sampling frame for area probability surveys,
telephone surveys,
questionnaire design,
variance estimation for complex surveys,
analysis of contingency tables, and
regression analysis for survey data.

STATISTICS 33900=FINM 33100. Financial Data Analysis.
Sec 01: Per A. Mykland, W, 6:00-9:00 PM, Eckhart 202
PQ: Math Finance Students only
Required reading:
Recommended textbooks:
Applied Linear Regression (3rd edition) by Sanford Weisberg (Wiley)
Time Series: Applications to Finance by Ngai Hang Chan (Wiley)
Statistical Analysis of Financial Data in S-Plus by Rene A. Carmona (Springer-Verlag)
Mathematical Statistics and Data Analysis by John A. Rice (Duxbury)
The Basics of S-plus, by Andreas Krause and Melvin Olson (Springer-Verlag)

Note that only part of each book will be used in the course. However, in the course of your career, you will find all of them useful to have on your shelf for further reading and reference.

STATISTICS 34300. Applied Linear Stat Methods.
Sec 01: Mathias Drton, TTH, 9:00-10:20 AM, Eckhart 133
PQ: STAT 24500 and MATH 25000 or equivalent
Optional Reading: Venables, W.N. and Ripley, B.D. (1999). Modern Applied Statistics with S-Plus (3rd ed). Springer-Verlag.
Required Reading: Weisberg, S. (2005). Applied Linear Regression, Third Edition. John Wiley & Sons. Software: Splus or R.

STATISTICS 35000=HSTD 33300, ENST 27400, PPHA 36400. Principles of Epidemiology.
Sec 01: Kurina Lianne , TTH, 9:00-10:20 AM, BSLC 240
PQ: Introductory Statistics
Required reading:

STATISTICS 35600. CANCELLED .

Back to to

STATISTICS 36900=HSTD 33300. Longitudinal Data Analysis.
Sec 01: Paul Rathouz, TTH, 9:00-10:20 AM, BSLC 313
PQ: HSTD 32100; STAT 22000 or equivalent and HSTD 32400-STAT 22400 or equivalent; or consent of instructor.
Required reading: *Fitzmaurice GM, Laird NM, Ware JH. (2004). Applied Longitudinal Analysis. Hoboken, NJ: Wiley.
Other references: (* Indicates text on reserve in Eckhart Library.)
   *Diggle PJ, Heagerty P, Liang K-Y, & Zeger SL. (2002). Analysis of Longitudinal Data, 2nd edn.
Oxford: Oxford University Press.
   *Hedeker DR & Gibbons RD. (2006). Longitudinal data analysis. Hoboken, NJ: Wiley-Interscience.
Hand D, Crowder M. (1996). Practical Longitudinal Data Analysis. London: Chapman & Hall.
   *Lindsey JK. (1999). Models for repeated measurements. New York: Oxford University Press.
Littel RC, Milliken GA, Stroup WA, Wolfinger RD. (1996). SAS System for Mixed Models. Cary,
NC: SAS Institute.

STATISTICS 37610. Monte Carlo Methods in Scientific Computing.
Sec 01: Samuel Kou, TTH, 1:30-2:50 PM, Eckhart 117
PQ: Consent of instructor
Required reading: Jun S. Liu (2001). Monte Carlo Strategies in Scientific Computing. Springer.

This course introduces students to modern Monte Carlo methods and their applications in scientific computing. In addition to the classical topics, such as Metropolis-Hastings algorithm, Gibbs sampler, importance sampling and sequential Monte Carlo, special topics include Swendsen-Wang algorithm, multicanonical sampling, histogram method, multigrid sampling, parallel tempering, equi-energy sampler, fragment-regrowth sequential sampling, dimension expansion Monte Carlo, DNA sequence alignment and motif finding, lattice protein folding, and polymer models on lattice.

STATISTICS 37910=CMSC 35510. Statistical Methods in Computer Vision.
Sec 01: Yali Amit, TTH, 10:30-11:50 AM, Ryerson 277
PQ: Consent of instructor
Required reading: Instructor’s notes and selected papers.

STATISTICS 38100. Measure-Theoretic Probability I.
Sec 01: Michael Wichura, MWF, 2:30-3:20 PM, Eckhart 117
PQ: STAT 31300 or consent of instructor
Required Reading: There is no text required or recommended. Notes will be provided.

SUMMER 2007

STATISTICS 22000. Stat Meth And Applications.
Sec 01: David Matteson, TTH, 1:00-3:00 PM, Eckhart 117
PQ: Math 15200 or equivalent
Required reading: Moore, D. S. and McCabe, G. P. (2006). Introduction to the Practice of Statistics, 5th Edition. Freeman.

This course is an introduction to statistical techniques and methods of data analysis, including the use of computers. Examples are drawn from the biological, physical, and social sciences. Students are required to apply the techniques discussed to data drawn from actual research. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, one- and two-sample problems, the analysis of variance, linear regression, and analysis of discrete data.

SPRING 2007

STATISTICS 22200. Linear Models and Experimental Design.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 133.
PQ: STAT 22000 or consent of instructor.
Required Reading: Oehlert, G. W. (2000) A First Course in Design and Analysis of Experiments. W. H. Freeman. ISBN: 0-7167-3510-5.

This course will introduce the student to the major statistical issues in the design of experiments and the analysis of experimental data. The major topics will be

The basic principles of experimental design: randomization, blocking,
and balance.
Important classes of designs: matched pairs, complete factorial designs,
and fractional factorial designs.
Analysis of variance and inference from experimental data.

In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment of their own.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Oli Atlason, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Math 13300, 15300 or 16300.
Required Reading: Chance and Rossman (2005). Investigating Statistical Concepts, Applications, and Methods, First Edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24500. Statistical Theory/Method-2.
Sec 01: Stephen M. Stigler, TTH, 1:30-2:50 PM, Location: Eckhart 133.
PQ: STAT 23400 and STAT 23500 or STAT 24400 or consent of instructor.
Required Reading: Rice, J. A. Mathematical Statistics and Data Analysis, 3rd ed., Duxbury Press. ISBN: 0-534-20934-3.

STATISTICS 24600. Statistics Theory/Method-3.
Sec 01: Yali Amit, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: STAT 24400 and STAT 24500 or consent of instructor.
Required Reading: Wasserman, Larry (2003). All of Statistics, Springer. ISBN: 0-387-40272-1.

This course will introduce a variety of modern statistical methods. We will start with the description of families of multivariate models (multivariate normal distribution, graphical models, log-linear models). We will then discuss Missing data problems and the EM algorithm, Bayesian inference, Monte-carlo simulations, bootstrap and some modern classification techniques.

STATISTICS 25100. Intro to Math Probability.
Sec 01 and 02: Michael J. Wichura and Gregory F. Lawler. Professor Wichura will teach both sections 01 and 02 for the first half of the course; Professor Lawler will teach both sections
the second half of the course.
Time: TTH, 12:00-1:20 PM (Section 01). TTH, 3:00-4:20 PM (Section 02).
Location: Eckhart 312.
PQ: MATH 20000 or MATH 20500 or consent of instructor.
Required Reading: Ross, A First Course in Probability, 7th Edition. Pearson/Prentice Hall. ISBN 0-13-185662-6.

The aim of the course is to provide an introduction to the concepts of probability. The course will cover the basic ideas used to describe aspects of randomness, such as events, random variables, independence, and conditional probability, with emphasis on the methods, calculation, and applications of probability. The topics treated are: combinatorics; probability models; rules of probability; conditional probability; independence; random variables; expectation and standard deviation; games of chance; common discrete distributions—uniform,
binomial, hypergeometric, geometric, negative binomial, and Poisson—and their interrelationships; univariate and multivariate density and distribution functions, change of variable formulas for densities, common continuous distributions—beta, Cauchy, chisquare, exponential, gamma, lognormal, normal, uniform—and their interrelationships; moment generating functions, laws of large numbers and the central limit theorem.

Midterm: Please note that there will be a COMMON midterm for both sections, to be held THURSDAY EVENING of the sixth week (May 3), from 6:00-8:00PM

For more information, please visit http://www.stat.uchicago.edu/~wichura/Stat251/courseinfo.html

STATISTICS 26700=STAT 36700=HIPS 25600=CHSS 32900. History of Statistics.
Sec 01: Stephen M. Stigler, MWF, 9:30-10:20 AM, Eckhart 203.
PQ: A course in Statistics.
Recommended Reading: Stephen M. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900. (Cambridge, Mass.: Harvard University Press, 1986.) (Available in paperback.) Other materials will be distributed in class or by web.

STATISTICS 30200. Mathematical Statistics-2.
Sec 01: Wei-Biao Wu, MW, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30100 or consent of instructor.
Required Reading: Casella and Berger. Statistical Inference, Second edition. Duxbury Press. ISBN: 0-534-24312-6

STATISTICS 31300. Intro: Stochastic Processes-2.
Sec 01: Steven P. Lalley, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: STAT 31200 or consent of instructor.
Required Reading: Notes will be posted online.

STATISTICS 32300. Bayesian Methods in Biostatistics.
Sec 01: Vanja Dukic, Wed, 12:30-3:20 PM, BSLC
PQ: STAT 30100-30200, 24400-24500, 34300, 31200-31300, consent of instructor.
Required Reading: Tanner, 3rd ed., Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, Springer.

STATISTICS 33200=HSTD 43200. Causal Inference.
Sec 01: Tyler J. VanderWeele, MW, 10:30-11:50 AM , BSLC
PQ: The course is intended for both masters and doctoral students in statistics and in the social sciences. The course will be accessible to anyone with a firm understanding of linear and logistic regression (e.g. STAT 224/226 or HSTD 324/327) though students would benefit from a more sophisticated understanding of statistics. Supplementary handouts will be provided covering the proofs and technical details concerning methods presented for more theoretically-oriented students.
Required Reading: None. If you want to read further on certain topics, take a look at Judea Pearl's book "Causality".

The course will be concerned with the process of drawing causal inferences from observational data in the biomedical and social sciences. The course will introduce a number of fundamental concepts in causal inference and cover methods of estimating causal effects for both point- and time-varying exposures. Concepts and methods that will be covered include: confounding, potential outcomes, propensity scores, directed acyclic graphs, inverse probability of treatment weighting, marginal structural models and structural nested models. Time permitting, the course may also briefly survey a number of other topics such as instrumental variables, the estimation of direct and indirect effects, sensitivity analysis and bounds for causal effects.

STATISTICS 33700=GSBC 41914=ECON 31500. Multivariate Time Series Anal.
Sec 01: Ruey-Shiong Tsay, Wed, 8:30-11:30 AM
PQ: Business 41910 or equivalent; = STAT 33700 and ECON 31500
Required Reading: None.

Course website: http://faculty.chicagogsb.edu/ruey.tsay/teaching/mts

A useful web site for U.S. data: Fed. Res. at St Louis: http://research.stlouisfed.org/fred2/

Course Objective:
1. To study the basic theory of multivariate processes
2. To gain experience in analyzing multivariate time series data
3. To learn multivariate time series models, including vector AR and ARMA models with exogenous variables
4. To understand co-integration and error-correction models
5. To study structural specification of a vector process
6. To learn state-space models and Kalman filter.

No textbook is assigned. Lecture Notes available on course web.

Grading:
Mid-term (40%), Final project (40%), and homework assignments (20%).

Computing:
The key software packages are SCA and S-Plus, but you may use any software of your choice.

Course Outline: All topics include applications
1. Transfer function models
2. Stationary vector autoregressive and moving average models
3. Estimation, modeling, and forecasting
4. Unit-root, co-integration and error-correction models
5. Multivariate Seasonal models
6. Structural specification
7. State-space model and Kalman filter
8. Multivariate volatility models if time permits.

STATISTICS 34700. Generalized Linear Models.
Sec 01: Peter McCullagh, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 34300 or consent of instructor.
Required Reading: McCullagh & Nelder, Generalized Linear Models, 2nd edition. Chapman & Hall/CRC. ISBN: 0-412-31760-5.
Recommended Reading: Venables & Ripley, Modern Applied Statistics w/S, 4th edition, Springer. ISBN: 0-387-95457-0.
Cox & Snell (2000), Applied Statistics, Chapman & Hall/CRC. ISBN: 0-412-16570-8.

STATISTICS 35500. Statistical Genetics.
Sec 01: Mary Sara McPeek, Friday, 1:30-4:00 PM, Eckhart 117.
PQ: Consent of instructor.
Required Reading: None.

This is an advanced course in statistical genetics. Prerequisites are Human Genetics 47100 and Statistics 24400 and 24500. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor. This is a discussion course and student presentations will be required. Topics vary and may include, but are not limited to, statistical problems in association mapping, linkage mapping, population genetics, microarray analysis, and genetic models for complex traits.

STATISTICS 36700=STAT 26700=CHSS 32900=HIPS 25600. History of Statistics.
Sec 01: Stephen M. Stigler, MWF, 9:30-10:20 AM, Eckhart 203.
PQ: A course in Statistics.
Recommended Reading: Stephen M. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900. (Cambridge, Mass.: Harvard University Press, 1986.) (Available in paperback.) Other materials will be distributed in class or by web.

STATISTICS 37800. Statistical Computing.
Sec 01: Dongping Fang, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: Experience with R or Splus programming.
Required Reading: None.

This course emphasizes practical aspects of designing statistical algorithms for implementation. Throughout the course, some basic computer arithmetic, accuracy, linear algebra and optimization methods are covered, some selected algorithms like linear regressions, multinomial logistic regression, neural networks, classification tree, clustering will also be introduced and used for illustration. Note that this course doesn't cover program languages like C++, Java, etc.

References:

Lange, K. (1999). Numerical Analysis for Statisticians. Springer.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1992). Numerical Recipes in C: The Art of Scientific Computing. (or Numerical Recipes in Fortran: The Art of Scientific Computing.) Second Edition. Cambridge University Press.
Thisted, R. A. (1988). Elements of Statistical Computing. Chapman and Hall.
Gray, R. (2003), Advanced Statistical Computing (BIO 248 cd Course Notes, http://www.stat.wisc.edu/~mchung/teaching/stat471/stat_computing.pdf)
Micah Altman, M., Gill, J., and McDonald, M. P. (2004). Numerical Issues in Statistical Computing for the Social Scientist. John Wiley & Sons

STATISTICS 38600. Topics in Stochastic Processes.
Sec 01: Steven P. Lalley, TTH, 3:00-4:20 PM,Eckhart 117
PQ: STAT 23400 and STAT 23500 or STAT 24400 or consent of instructor.
Required Reading:
Topics:

Wiener process (Brownian motion)
Weak Convergence in Function Space
Ito Integral and Ito Calculus
Introduction to Diffusion Processes

WINTER 2007

STATISTICS 22600=HSTD 32600. Analysis of Categorical Data.
Sec 01: Mei Wang, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 22000 or equivalent.
It is expected that the students have a good understanding of basic descriptive statistics such as means, variances and expectation, of the inferential notions of estimate, confidence intervals and significance or hypothesis testing. Familiarity with one statistical package, e.g. Stata, Sas, Splus or R, Spss, Minitab and ability to access Web sites and to download files from the Web are required. Stata will be used in this course for Winter 2007.
Required Reading:
Agresti, A. An introduction to Categorical Data Analysis. Wiley, 1996.

This course is an introduction to the theory and applications of statistical methods for investigating the relationships among discrete variables. The course will present methods for analyzing categorical data, standard methods for contingency tables such as odds ratios, tests of independence and various measures of association, generalized linear models for binary data and count data, logistic regression for binomial data, loglinear models for Poisson data. The statistical techniques discussed will be presented by many real examples involving both physical and social science data.

STATISTICS 22700=HSTD32700. Biostatistical Methods.
Sec 01: Ronald A. Thisted, TTH, 10:30-11:50 AM, BSLC Arr
PQ: HSTD 32400/STAT 22400 or equivalent; or consent of instructor.
Reading:

This course is designed to provide students with tools for analyzing categorical, count and time-to-event data frequently encountered in medicine, public health and related biological and social sciences. The course will emphasize application of the methodology rather than statistical theory, including recognition of the appropriate methods, interpretation and presentation of results. Methods covered include: contingency table analysis, Kaplan-Meier survival analysis, Cox proportional-hazards survival analysis, logistic regression, Poisson regression.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300
Required Reading: Chance and Rossman (2005). Investigating Statistical Concepts, Applications, and Methods, First Edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.
Downing and Clark (1996). Forgotten Statistics. Barron's Educational Series, ISBN: 0-8120-9713-0

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24400. Statistical Theory and Methods I.
Sec 01: Michael Stein, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: MATH 19600, 20100, or 20500
Required Reading: Rice, John A. (2007). Mathematical Statistics and Data Analysis, Third Edition, by (Duxbury).

There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited and brief, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation.

The mathematics prerequisites are listed as general guidance. You should be comfortable with multivariate calculus through partial differentiation and multiple integration.

STATISTICS 24500. Statistical Theory and Methods II.
Sec 01: Wei Biao Wu, TTH, 1:30-2:50 PM, Eckhart 133 .
PQ: STAT 24400 or consent of instructor.
Required Reading: Rice, J. A. (1995). Mathematical Statistics and Data Analysis, 2nd ed., Duxbury.

STATISTICS 25200=STAT 31200. Introduction to Stochastic Processes I.
Sec 01: Steven P. Lalley/Per A. Mykland, TTH, 10:30-11:50 AM, Eckhart 312.
PQ: STAT 25100 or consent of instructor
Required Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

STATISTICS 25300=STAT 31700. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 133.
PQ: Consent of instructor
Required Reading:Ross, R. (2003). Introduction to Probability Models, 9th ed.

This course introduces stochastic processes as models for a variety of phenomena in the physical and biological sciences. Following a brief review of basic concepts in probability the course will introduce stochastic processes that are popular in applications in sciences, such as discrete time Markov chain, the Poisson process, continuous time Markov process, renewal process and Brownian motion.

STATISTICS 30100. Mathematical Statistics I
Sec 01: Mary Sara McPeek, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30400 and MATH 20500 or consent of instructor
Required Reading: Casella and Berger. Statistical Inference, 2nd ed.
Ferguson. A Course in Large Sample Theory.

STATISTICS 31200=STAT 25200. Introduction to Stochastic Processes I.
Sec 01: Steven P. Lalley/Per A. Mykland , TTH, 10:30-11:50 AM, Eckhart 312.
PQ: STAT 25100 or consent of instructor
Required Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

STATISTICS 31700=STAT 25300. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 133.
PQ: Consent of instructor
Required Reading: Ross, R. (2003). Introduction to Probability Models, 9th ed. or most current.

STATISTICS 32500=GSBC 41902. Statistical Inference.
Sec 01: Nicholas G. Polson, Wed 8:30-11:30 AM, GSB C10.
PQ: Business 41901=STAT 32500
Reading: DeGroot and Scherviah, Probability and Statistics. Lecture notes will be provided in the form of a CoursePack.

STATISTICS 33500=GSBC 41910=ECON 31400. Time Series Analysis.
Sec 01: Jeffrey R. Russell, Fri 1:30-4:30 PM, GSB HP3B.
PQ: Business 41901 or consent of Instructor
Reading: Hamilton, “Time Series Analysis”, is available at the bookstore.
Software: We will use the student version of the Eviews software.
Here is the the company: http://www.eviews.com/
The student version is here: http://www.eviews.com/eviews4/eviews41s/evstud41.html

Topics Covered:

Difference Equations and Lag Operators
Stationary ARMA models
Maximum Likelihood Estimation and Inference
Spectral Analysis
Forecasting and Forecast Evaluation
GARCH and Stochastic Volatility Models for Time Varying Volatility
Unit Roots and Time Trends
Vector Autoregressions (VARs)
Cointegration

Grades: Grades will be determined by homework (15%), a midterm (35%) and a final
exam (50%).

STATISTICS 33800. Statistical Inference for Financial Data.
Sec 01: Per A. Mykland, TTH, 1:30-2:50 PM, Ryerson 358.
PQ: Consent of instructor
Required Reading:

Financial data is commonly modeled by diffusion, jump-diffusion, and related models, and it is usually supposed that observation is discrete. The course is concerned with inference in such settings. We shall be reading papers, and also get some of the mathematical background from the texts. We shall not focus so much on the financial application, but rather the econometrics of these data.

The format is a mixture of lectures and student presentations.

The course is primarily intended for second year graduate students in Statistics, and also students with similar background in Econometrics or Finance. It is recommended, but not absolutely required, that students have taken Stat 30400-30100-30200 and either Stat 31200-31300 or Stat 38100-38300. Equivalent courses are also OK. If you have never taken a finance course, you may consider taking one concurrently (whether in the GSB, Economics, or Statistics), though this is not required.

STATISTICS 34500. Design and Analysis of Experiments.
Sec 01: Michael L. Stein, MW, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 34300
Reading: Mead, R., The Design of Experiments.

Course work will include the planning, execution and analysis of an experiment by the class.

STATISTICS 35201=HSTD 32901. Introduction to Clinical Trials.
Sec 01: James Dignam, TTH, 1:30-2:50 PM, BSLC.
PQ: HSTD 32100; STAT 22000; introductory statistics; or consent of instructor
Reading:

This course will review major components of clinical trial conduct, including the formulation of clinical hypotheses and study endpoints, trial design, development of the research protocol, trial progress monitoring, analysis, and the summary and reporting of results. Other aspects of clinical trials to be discussed include ethical and regulatory issues in human subjects research, data quality control, meta-analytic overviews and consensus in treatment strategy resulting from clinical trials, and the broader impact of clinical trials on public health.

STATISTICS 35700=HSTD 31001. Epidemiologic Methods.
Sec 01: Diane Lauderdale, Ronald Thisted, TTH, 12:00-1:20 PM, Location: BSLC 202.
PQ: HSTD 30900 or consent of instructor
Reading:

STATISTICS 37700. Statistical Machine Learning.
Sec 01: Gilles Blanchard, TTH, 3:00-4:20 PM, Eckhart 117.
PQ: Consent of instructor
Reading: The course will borrow material from different sources, including:

L. Devroye, L. Gyorfi, G. Lugosi, A probabilistic Theory of Pattern recognition, Springer

R.Duda, P.Hart, D.Stork, Pattern classification, Wiley

Advanced lectures on machine learning, Springer lecture notes in computer science (2 volumes)

There is no *required* reading. The following source is recommended:

G. Lugosi, Pattern classification and learning theory.
in Principles of nonparametric learning, Springer, p. 1-56, also available at
http://tornasol.upf.es/~lugosi/lecturenotes.ps.

This course will have two main focuses: presenting different methods of machine learning and introducing the main mathematical tools of statistical learning theory, which allow to study these methods from a statistical perspective.

Machine learning applies to complex data for which standard statistical approaches are not appropriate, for example in very high dimension or for higly structured, non numerical data. We will introduce several popular machine learning methods, such as nearest-neighbor, decision trees, Support Vector Machines and other kernel methods, ensemble and boosting methods.

Statistical learning theory is a generic statistical and mathematical framework into which the performance of the above methods can be studied, and for which the notion of model complexity plays a central role. We will introduce traditional learning theory, Vapnik-Chervonenkis dimension, as well as some more recent developments such as Rademacher complexities and randomized approaches.

Depending on the interest of the students the emphasis can shift on one or the other part. A solid background in basic probability and undergraduate level math is expected.

Statistics 39100=FINM 34600. Stochastic Calculus/Finance II
Sec 01: Steven P. Lalley/Per A. Mykland, W, 6:00-9:00 PM, Eckhart 202.
PQ: Math Finance Students Only
Required Reading: Stochastic Calculus for Finance I-II, by Steven E. Shreve (required).
A Course in Financial Calculus, by Alison Etheridge (highly recommended).
`The basics of S-PLUS'', 3rd edition, by the same authors (A. Krause and M. Olson), ISBN 0-387-95456-2 (required).

This course is an introduction to stochastic calculus as it is relevant to the pricing and hedging of
options and other derivative securities. It is the first of a two-quarter sequence offered in collaboration
by the Department of Statistics and the master's program in Mathematical Finance. The main topics to
be covered are:

The Fundamental Theorem of Asset Pricing
Martingales
Brownian Motion
The Ito Integral and Ito's Formula
The Black-Sholes Formula
Girsanov's Formula
Currency Options
The Martingale Representation and Hedging

There will be weekly homework assignments, and midterm and final exams. The course assistants will conduct weekly help sessions on Friday afternoons.

STATISTICS 47900. Stochastic Models for Memory and Learning.
Instructor: Yali Amit

Time: MW, 2:30-3:50 PM
Location: Eckhart 117
PQ: Consent of instructor
Required Reading: None

AUTUMN 2006

STATISTICS 22400=HSTD 32400. Applied Regression Analysis.
Sec 01: Vanja M. Dukic, TTH, 10:30-11:50 AM,Eckhart 133.
PQ: STD 32700 or STAT 22000 or STAT 23400 or STAT 24400 or consent of instructor.
Required Reading: Chatterjee, Hadi, and Price, Regression Analysis by Example, 4th Edition. (Note: the 3rd edition can also be used.)
STATA help sessions:
Note that these sessions are optional, and are designed to help you get familiar with Stata enough to solve homework problems (bring your questions!). All sessions within one week are identical. All notes and worksheets used in these sessions will be posted on this website. We have 3 sessions, in hope that each of you can fit one every week into your schedules:
Mondays 12pm-1pm in BSLC 018.
Tuesdays 12pm-1pm in BSLC 018.
Wednesdays 2pm-3pm in BSLC 018.

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Omar De la Cruz, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300
Required Reading: Chance and Rossman (2005). Investigating Statistical Concepts, Applications, and Methods, First Edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24400. Statistical Theory and Methods I.
Sec 01: Mathias Drton, TTH, 1:30-2:50 PM, Kent 107.
PQ: MATH 19600, 20100, or 20400
Required Reading: Rice, John A. (1995). Mathematical Statistics and Data Analysis, Second Edition, by (Duxbury).

There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited and brief, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation. Statistical software will be used for simulations and data analysis.

STATISTICS 30400. Distribution Theory.
Sec 01: Dan Nicolae, MWF, 1:30-2:20 PM, Eckhart 117.
PQ: STAT 24500 & MATH 25000 or equivalent
Recommended Reading: Severini, T. (2005). Elements of Distribution Theory. Cambridge University Press.

This course covers the basics of distribution theory. Topics include:

Distribution functions and their inverses, quantile functions, Q/Q plots, change-of-variables for probability densities
Expectation, variance, median, mode of random variables
Basics of measure theory, including Fubini's theorem and interchangeability of limits and integrals
Moment generating functions and characteristic functions, including power series expansion, inversion formulas, uniqueness theorems, and convergence in distribution
Cumulants and cumulant generating functions: examples, properties
Different concepts of convergence for random variables
Limit theorems, including the weak law of large numbers, the central limit theorem.

STATISTICS 30700=CMSC 37800. Numerical Computation.
Sec 01: Yali Amit, MWF, 10:30-11:20 PM, Eckhart 117.
PQ: Consent of instructor and elementary programming experience
Required Reading: Watkins, Fundamentals of Matrix Computation, Wiley.

We present a rigorous development of the fundamental algorithms for the decomposition of matrices, with applications to least squares problems, and finite dimensional eigenvalue problems. Most of the relevant mathematics will be developed rigorously. Some basics of iterative methods and non-linear optimization will be presented. Time permitting we will also describe the Fast Fourier Transform and the Discrete Wavelet transform. Some basics of C++ programming will be introduced through the implementation of the algorithms.

Topics include:

Gaussian elimination and back-substitution
LU decomposition. (General/Symmetric)
Singular value decomposition. (general/Symmetric)
Householder orthogonalization and QR factorization. (General/Symmetric).
Jacobi transformations for symmetric matrices.
QR decompositions.
Generalized inverses.
Elementary eigenvalue methods for symmetric matrices
Iterative methods: Jacobi and Gauss Seidel.
Conjugate gradient.
Dynamic programming.
The FFT and the Discrete wavelet transform.

A common set of C++ objects will be defined at the start of the course, meant to deal with arrays, and the students will write code for most of the algorithms in C++.

STATISTICS 32400=GSBC 41901. Probability and Statistics.
Sec 01: Nicholas G. Polson, Th, 8:30-11:30 AM, Lecture Hall C10.
PQ: 1 Year of Calculus.
Required Reading: DeGroot and Schervish. Probability and Statistics. Lecture notes will be available in the form of a CoursePack.

This Ph.D.-level course (in addition to 41902) provides a thorough introduction to Classical and Bayesian statistical theory. The two-quarter sequence provides the necessary probability and statistical background for many of the advanced courses in the GSB curriculum. The central topic of Business 41901 is probability. Basic concepts in probability are covered. An introduction to martingales is given. Homework assignments are given throughout the quarter.

STATISTICS 32600=GSBC 37904=ECON 31601. Marketing Topics: Bayesian Applications in Marketing and MicroEconometrics.
Sec 01: Peter E. Rossi, W, 6:00-9:00, Lehman Brothers Classroom-HPC02.
PQ:
Required Reading:

This course will cover a comprehensive introduction to Bayesian inference with special emphasis on micro-data and marketing applications. The course will be based on the instructor's textbook. Topics include: Bayesian Essentials, Practical MCMC methods, Hierarchical Models, Non-standard Priors, Models for data with Discrete Components, Bayesian treatment of Simultaneity, and Dirichlet Process Priors. The course will also emphasize statistical computing in R. For all models and topics discussed in the course, examples of R/C code will be provided. In the homeworks, students will be asked to modify existing R code and write their own code to extend some of the ideas covered in class. R is the most important statistical language which is similar to, but with more extensive capababilites, as MATLAB.

STATISTICS 33100. Sample Surveys.
Sec 01: Kirk Wolter, TTH, 10:30-11:50 AM,Eckhart 117.
PQ: Consent of instructor
Reading: Cochran, W. G. (1977). Sampling Techniques, Third Edition. Wiley; Wolter, K.M. (1985). Introduction to Variance Estimation, Springer-Verlag, New York.

This is an introductory course to the statistics and methodology of sample surveys. Topics include

basic methods of sample selection,
determining sample size, stratification,
general estimators (Horvitz-Thompson, ratio, generalized regression, calibration),
domain estimation,
nonresponse,
nonsampling error,
multiple-stage sampling,
a national sampling frame for area probability surveys,
telephone surveys,
questionnaire design,
variance estimation for complex surveys,
analysis of contingency tables, and
regression analysis for survey data.

STATISTICS 34300. Applied Linear Stat Methods.
Sec 01: Mathias Drton,TTH, 9:00-10:20 AM, Eckhart 133.
PQ: STAT 24500 & MATH 25000 or equivalent
Optional Reading: Venables, W.N. and Ripley, B.D. (1999). Modern Applied Statistics with S-Plus (3rd ed). Springer-Verlag.
Required Reading: Weisberg, S. (2005). Applied Linear Regression, Third Edition. John Wiley & Sons.
Software: Splus or R.

STATISTICS 35000=HSTD 30900=ENST 27400=PPHA 36400. Principles of Epidemiology.
Sec 01: Lianne Kurina, TTH, 9:00-10:20 AM, BSLC (to be announced).
PQ: Introductory statistics recommended, may be taken concurrently
Required Reading:

Epidemiology is the study of the distribution and determinants of health and disease in human populations. This course introduces the basic principles of epidemiologic study design, analysis, and interpretation, through lectures, assignments, and critical appraisement of both classic and contemporary research articles. The course objectives include: (1) To be able to critically read and understand epidemiologic studies; (2) To be able to calculate and interpret measures of disease occurrence and measures of disease-exposure associations; and (3) To understand the contributions of epidemiology to clinical research, medicine and public health.

STATISTICS 36900=HSTD 33300. Longitudinal Data Analysis.
Sec 01: Paul Rathouz, TTH, 9:00-10:20 AM, BSLC (to be announced).
PQ: HSTD 32100; STAT 22000; or equivalent, and HSTD 32400/STAT 22400 or equivalent; or consent of instructor
Required Reading: Diggle, P.J., Heagerty, P., Liang, K.-Y., & Zeger, S.L. (2002). Analysis of Longitudinal Data, Second Edition. Oxford: Oxford University Press.

Longitudinal data consist of multiple measures over time on a sample of individuals. This type of data occurs extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. While some theoretical statistical detail is given (at the level appropriate for a Master's student in Statistics), the primary focus will be on data analysis and interpretation. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies .

STATISTICS 38100. Measure-Theoretic Probability I.
Sec 01: Michael J. Wichura, MWF, 2:30-3:20 PM, Eckhart 117.
PQ: STAT 31300 or consent of instructor
Required Reading: There is no text required or recommended. Notes will be provided.

SPRING 2006

STATISTICS 22200. Linear Models and Experimental Design.
Sec 01: Mei Wang, TTH, 9:00-10:20 AM, Eckhart 133.
PQ: Statistics 22000 or consent of instructor
Required Reading: Oehlert, G. W. (2000) A First Course in Design and Analysis of Experiments. W. H. Freeman. ISBN: 0-7167-3510-0
Optional Reading: Hamilton, L. C. (2003) Statistics with Stata (Updated for Version 7). Duxbury Press.

This course will introduce the students to the major statistical issues in the design of experiments and the analysis of experimental data. The major topics will be

The basic principles of experimental design: randomization, blocking, and balance.
Important classes of designs: matched pairs, complete factorial designs, Latin squares, Graeco-Latin squares, fractional factorial designs and confounding.
Analysis of covariance and inference from experimental data.

In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment of their own.

STATISTICS 23400. Statistical Models and Methods I.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Linda B. Collins, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300
Required Reading: Chance and Rossman (2006). Investigating Statistical Concepts, Applications, and Methods, 1st edition. Duxbury (Thomson Brooks/Cole), ISBN: 0-4950-5064-4.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24600. Statistical Theory and Methods III.
Sec 01: Marc Coram, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: Statistics 23400 and Statistics 23500 or Statistics 24400 and 24500, or consent of instructor
Required reading:
Givens and Hoeting (2005). Computational Statistics. 1st edition. Wiley. ISBN: 0471-46124-5

This course is the third part of a sequence that introduces mathematical statistics and probability. In this quarter, the emphasis is on modern computer-intensive statistical methods.

In the first part of the course, the impact of missing data on statistical analyses is considered and algorithms for iterative maximum likelihood estimation, such as the EM and the Newton-Raphson algorithms, and for Bayesian computation, such as Data Augmentation, are introduced in certain examples. Additionally, Markov Chain Monte Carlo methods are outlines in a more general framework of Bayesian inference.

The second part of the course presents an overview of the bootstrap and related nonparametric methods for assessing statistical accuracy.

Knowledge of probability distributions, random variables, and estimation techniques such as maximum likelihood from Statistics 24400 and 24500 is essential for this class.

STATISTICS 25100. Introduction to Mathematical Probability.
Sec 01: Michael Wichura, TTH, 4:30-5:50 PM, Eckhart 133.
PQ: Math 20000 or 20400 or consent of instructor
Reading: Ross, Sheldon (2006), A First Course in Probability, Seventh Edition. Pearson/Prentice-Hall. ISBN: 0-13-185662-6

Probability theory originated in the consideration of gambling problems, but has become an important tool for scientists, engineers, medical practitioners, lawyers, and people working in business. A wide variety of phenomena are characterized by randomness and uncertainty, which is measured by probability. Probability models also play a fundamental role in the statistical analysis of data. The aim of the course is to provide an introduction to the elementary concepts of probability. The first part of the course will introduce the student to the basic ideas used to describe aspects of randomness, such as events, random variables, independence, and conditional probability. The remainder of the course focuses on the methods, calculation, and applications of probability. The topics treated are: combinatorics, discrete and continuous distributions, density functions, distribution theory, calculation and interpretation of moments, covariance and correlation, the classical central limit theorem.

STATISTICS 26100 Time Dependent Data.
Sec 01: Michael Stein, MWF 10:30-11:30 AM, Eckhart 117.
PQ: Students will need some exposure to linear modeling (Statistics 22400, 24500, or consent of instructor).
Reading: Janacek ((2001), Practical Time Series, Arnold, London. ISBN: 0-340-71999-0.
Chatfield, The Analysis of Time Series, Chapman & Hall/CRC. ISBN: 1-58488-317-0.

This course considers the modeling and analysis of data that are ordered in time. The main focus will be on quantitative observations taken at evenly spaced intervals and will include both time-domain and spectral approaches. Time permitting, statistical approaches to other data types, such as categorical observations or point processes, will be considered.

STATISTICS 30200. Mathematical Statistics II.
Sec 01: Marc Coram, TTH, 3:00-4:20 PM,   Eckhart 117
PQ:  Statistics 30100 or consent of instructor
Reading:  Casella and Berger. Statistical Inference, Second edition. Duxbury Press. ISBN: 0534243126

This course continues the development of mathematical statistics. Topics of importance include: statistical decision theory, admissability and the Neyman-Pearson lemma, formal hypothesis testing, comparison with conditional frequentist and Bayesian viewpoints, ancillarity, interval estimation, UMP tests and MLR, unbiased tests, score statistics, generalized liklihood ratio tests and asymptotics, minimax estimation, empirical Bayes and "shrinkage".

STATISTICS 30800. Advanced Statistical Inference II.
Sec 01: Dan Nicolae, TTH, 9:00-10:20 AM, Eckhart 117.
PQ: Consent of instructor
Reading: None

The focus of the course will be on non-parametric statistics and statistical learning.

Topics for discussion include

Classical Nonparametric Statistics
Empirical Likelihood
Resampling Techniques
Supervised and Unsupervised Learning

Actual topics covered will depend on interests of students and instructor.

STATISTICS 31300. Introduction to Stochastic Processes II.
Sec 01: Steven Lalley, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: Statistics 31200 or consent of instructor
Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

This course is a continuation of Statistics 31300: Introduction to Stochastic Processes I. Topics to be discussed will include generating functions, Galton-Watson processes, continuous-time Markov chains, martingales, and Brownian motion. There will be weekly homework assignments, and midterm and final exams.

STATISTICS 34700. Generalized Linear Models.
Sec 01: Peter McCullagh, MW, 1:30-2:50 PM, Ryerson 352.
PQ: Statistics 34300 or consent of instructor
Reading: McCullagh & Nelder (1989). Generalized Linear Models, Second Edition. Chapman & Hall/CRC. ISBN: 0-412-31760-5
Venables and Ripley. Modern Applied Statistics W/S, Fourth Edition. Springer. ISBN: 0-387-95457-0
Cox & Snell (2000). Applied Statistics, Chapman & Hall/CRC. ISBN: 0-412-16570-8

This is an applied course for students who are familiar with linear models at the level of Draper and Smith or Weisberg. The following topics will be covered:

Factors, variates, contrasts, interactions
Exponential-family models: variance function
Definition of a generalized linear model: link functions
Analysis of deviance
Specific examples of GLMs

logistic and probit regression
cumulative logistic models
log-linear models and contingency tables
inverse linear models

Quasi-likelihood and least squares; estimating functions
Over-dispersion
Partially linear models.

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STATISTICS 35500. Statistical Genetics.
Sec 01: Mary Sara McPeek, F, 1:30-3:50 PM, Eckhart 117.
PQ: Human Genetics 47100 and Statistics 24400 and 24500 or consent of instructor
Reading:

This is an advanced course in statistical genetics. Prerequisites are Human Genetics 471 and Statistics 244 and 245. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor. This is a discussion course and student presentations will be required. Topics vary and may include, but are not limited to, statistical problems in linkage mapping, association mapping, map construction, and genetic models for complex traits.

STATISTICS 36300. Statistics for Dependent Data.
Sec 01: Wei Biao Wu, MW, 1:30-2:50 PM, Eckhart 117.
PQ: Statistics 30400, 38100 and 31200
Reading: See below.

Many deep statistical results have been obtained under the assumption that the observations are independent. However, applications from engineering, economics, geophysics, meteorology and other natural sciences suggest that the observations are dependent, and the dependence is the rule rather than the exception. So a statistical theory is needed to infer the underlying dependence structure for the purpose of interpretation, inference and prediction. In the course I will provide a novel look at the issue of dependence and introduce new dependence measures so that based on which a statistical theory can be built. In particular I will discuss parametric and nonparametric inference, time and frequency domain approaches, empirical processes, asymptotic representations, robust inference, unit root testing and other issues. New research problem will be proposed.

Readings:

Masanobu Taniguchi, Yoshihide Kakizawa (2000) Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics)
Fan J and Yao Q (2003) Nonlinear Time Series : Nonparametric and Parametric Methods (Springer Series in Statistics)
Wu W. B. (2005) "Nonlinear system theory: Another look at dependence", Proceedings of the National Academy of Sciences 102, 14150--14154
Wu W.B. (2005) On the Bahadur representation of sample quantiles for dependent
sequences. Ann. Stat. 1934-1963
Wei Biao Wu and Jan Mielniczuk (2002) Kernel density estimation for linear processes. Ann. Stat.1441-1459
Tong, Howell (1990) "Non-linear time series: a dynamical system approach" Oxford University Press

STATISTICS 37300. Graphical Models and Algebraic Statistics.
Sec 01: Mathias Drton, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: Consent of instructor
Reading: The course will draw on material from different sources, which will include:

D. Edwards (2000). Introduction to graphical modelling. 2nd edition. Springer, New York.
L. Pachter, B. Sturmfels (2005). Algebraic statistics for computational biology, Cambridge University Press.

The goal of this course is to provide an introduction to graphical models and algebraic techniques that are useful in the study of these models.

In graphical modelling, one associates, in a mathematically rigorous way, a statistical model with a graph: nodes represent variables and edges indicate some form of dependence between variables. This framework encompasses many classic statistical models and is popular in many applied areas, including in particular computational biology. Since the parameterizations and constraints defining graphical models are of polynomial nature, algebraic techniques introduced in the course can be applied to gain insight into structural and computational properties of a model.

A few weeks into the course students will form small research groups, each of which will work on a course project. Depending on student interests projects may be of applied, methodological, or theoretical nature. While some basic mathematical or statistical maturity is expected, students with different backgrounds are welcome to attend the class, but all are expected to work hard and collaborate with each other.

STATISTICS 38500. Advanced Topics in Probability: Brownian Motion and Stochastic Calculus.
Sec 01: Steven Lalley, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: Consent of instructor.
Reading: None.

Topics for Statistics 38500 will include:

Continuous-time martingales
Brownian motion
Weak convergence and Donsker's functional central limit theorem
Ito integral and stochastic calculus
Stochastic differential equatioins and diffusions

WINTER 2006

STATISTICS 22600=HSTD 32600. Analysis of Categorical Data.
Sec 01: Peter Radchenko, TTH 3:00-4:20 PM , Eckhart 133.
PQ: STAT 22000 or equivalent. It is expected that the students have a good understanding of basic descriptive statistics such as means, variances and expectation, of the inferential notions of estimate, confidence intervals and significance or hypothesis testing. Familiarity with a statistical package such as Stata, Sas, Splus, Spss, or Minitab is required.
Reading: Agresti, A. (1996). An introduction to Categorical Data Analysis, Wiley,
Optional Reading: Hamilton, L. C. (2003). Statistics with Stata (Updated for Version 8), Duxbury Press, 2003.

This course is an introduction to the theory and applications of statistical methods for investigating the relationships among discrete variables. It will present methods for analyzing categorical data, standard methods for contingency tables such as odds ratios, tests of independence and various measures of association, generalized linear models for binary data and count data, logistic regression for binomial data, loglinear models for Poisson data. The statistical techniques discussed will be presented by many real examples involving both physical and social science data.

STATISTICS 22700=HSTD 32700. Biostatistical Methods.
Sec 01: Ronald A. Thisted,TTH 10:30-11:50 AM, BSLC 202.
PQ: HSTD 32400/STAT 22400; or equivalent; or consent of instructor
Reading:

STATISTICS 23400. Statistical Models and Methods I.
Sec 01: Linda B. Collins, MWF, 9:30-10:20 AM, Kent 107.
PQ: MATH 13300, 15300 or 16300
Required Reading: Chance and Rossman (2006). Investigating Statistical Concepts, Applications, and Methods, Duxbury (Thomson Brooks/Cole), ISBN: 0-495-01655-1.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400..

STATISTICS 23500. Statistical Models and Methods II.
Sec 01: Linda B. Collins, MWF, 11:30-12:20 PM, Eckhart 133.
PQ: STAT 23400 or consent of instructor
Required Reading: Tamhane and Dunlop (2000). Statistics and Data Analysis: from Elementary to Intermediate, Prentice Hall, ISBN: 0-13-744426-5.

This is the second quarter of a two-quarter sequence. This course continues the presentation basic ideas of probability theory and statistics begun in STAT 23400, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Topics include repeated-sampling frequentist inference; consisting of methods for count data, ANOVA, and multiple regression, as well as an introduction to Bayesian inference. Additional topics, such as experimental design, non-parametric statistics and maximum likelihood estimation are introduced as time permits. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus, but is less demanding than that required by STAT 24400-24500. Other than the mathematical level, the content of the two sequences are similar.

STATISTICS 24500. Statistical Theory and Methods II.
Sec 01: Mathias Drton, TTH 1:30-2:50 PM, Eckhart 133.
PQ: STAT 24400 or consent of instructor
Recommended Reading: Rice, J. A. (1995). Mathematical Statistics and Data Analysis, 2nd ed., Duxbury.

STATISTICS 25300=STAT 31700. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: Consent of instructor
Reading: Ross, R. (2003). Introduction to Probability Models, 8th ed.

STATISTICS 30100. Mathematical Statistics I.
Sec 01: Mary Sara McPeek, TTH, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30400 or consent of instructor
Reading: Casella and Berger. Statistical Inference, 2nd ed.
Ferguson. A Course in Large Sample Theory.

STATISTICS 31200. Introduction to Stochastic Processes I.
Sec 01: Per A. Mykland, TTH, 10:30-11:50 AM, Eckhart 133.
PQ: STAT 25100 or consent of instructor
Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.

Stochastic processes provide models for random events that evolve in time which may include substantial dependence among observations at different times. The goal of this course is to present a variety of useful models including Markov chain, renewal theory, Brownian motions etc.

STATISTICS 31700=STAT 25300. Introduction to Probability Models.
Sec 01: Mei Wang, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: Consent of instructor
Reading: Ross, R. (2003). Introduction to Probability Models, 8th ed.

STATISTICS 33800. Statistical Inference for Financial Data
Sec 01: Per A. Mykland, TTH, 1:30-2:50 PM, Ryerson 277.
PQ: Consent of instructor
Reading:

The format is a mixture of lectures and student presentations.

STATISTICS 34500. Design & Analysis of Experiments.
Sec 01: Michael Stein, MW, 1:30-2:50 PM, Eckhart 133.
PQ: STAT 34300 or consent of instructor
Reading: Mead, R., The Design of Experiments.
Recommended Reading: Cox, D. R. and Solomon, P. J. Components of Variance.

An introduction to the methodology and application of linear models in experimental design. A major focus of the course will be the basic principles of experimental design, such as blocking, randomization and incomplete layouts. Many of the standard designs, such as fractional factorial, incomplete block and split unit designs will be studied within this context. The analysis of these experiments will be developed as well, with particular emphasis on the role of fixed and random effects. Time permitting, additional topics may include response surface analysis, the use of covariates in the analysis of designed experiments, and spatial analysis of field trials.

STATISTICS 37900=CMSC 35500=CMSC 25050. Topics in Computer Vision.
Sec 01: Yali Amit, WF, 2:30-3:50 PM, Ryerson 277.
PQ: Consent of instructor
Reading: Amit, Y. (2002). 2d Object Detection and Recognition: Models, Algorithms and Networks, MIT Press.

Deformable models for detecting objects in images will be discussed in detail. One-dimensional models to identify object contours and boundaries; two-dimensional models for image matching; sparse models for efficient detection of objects in complex scenes. Various mathematical tools needed to define the models and the associated algorithms will be developed. Applications include, detecting contours in medical images, matching brains, detecting faces in images and more. Methods for object recognition and classification related to the sparse detection models will be covered with applications to handwritten character recognition and recognition of rigid objects in scenes. Neural network implementations of some of the algorithms will be presented and some connections to the functions of the biological visual system will be discussed.

STATISTICS 38300. Measure-Theoretic Probability III.
Sec 01: Michael Wichura, MWF, 2:30-3:20 PM, Eckhart 117.
PQ: STAT 38100 or consent of instructor
Reading: No text book is required; notes will be distributed in class.

Topics for Stat 38300 will include:

The Hahn and Jordan decomposition theorems
Modes of convergence: with probability one, in probability, and in mean; uniform integrability
L2-spaces: projections; representation of linear functionals
The Radon-Nikodym theorem: absolute continuity, Radon-Nikodym derivatives; likelihood ratios; Lebesgue decompositions
Conditional expectation: given sub-sigma fields, and given measurable functions
Conditional probability: regular conditional probability distributions
Martingales: definitions and examples, transformations
Stopping times; optional sampling
Martingale limit and closure theorems
Backward submartingales
Continuous-time martingales: convergence, closure, optional sampling.

STATISTICS 39100=FINM 34600. Stochasti Calculus/Finance-2.
Sec 01: Jostein Paulsen, W, 6:00-9:00 PM, Eckhart 202.
PQ: Statistics 39000, FinMath 34500, MathFin Stds
Reading: Notes are made available on the internet.

The course will cover several issues in mathematical finance with main emphasis on fixed income models. Topics covered are

Models for short term interest rates
The Heath-Jarrow-Morton methodology
Forward measures, forward and future prices
Multivariate stochastic calculus
Change of numeraire, pricing of stock options with stochastic interest rates
Multivariate extensions of interest rate models
The LIBOR market model. Pricing of caps, caplets and swaptions

AUTUMN 2005

STATISTICS 22400=HSTD 32400. Applied Regression Analysis.
Sec 01: Vanja Dukic, TTH 10:30-11:50 AM, Eckhart 133.
PQ: HSTD 32700 or STAT 22000 or 23400 or 24400 or consent of instructor
Required Reading: "Regression Analysis by Example," (3rd Edition, Chatterjee, Hadi, and Price).

STATISTICS 23400. Statistical Models and Methods I.
Sec 01: Yali Amit, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Mathias Drton, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Math 13300, 15300 Or 16300
Required Reading: Tamhane and Dunlop (2000). Statistics and Data Analysis: from Elementary to Intermediate." Prentice Hall, ISBN: 0-13-744426-5.

Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for means and variances for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

STATISTICS 24400. Statistical Theory and Methods I.
Sec 01: Stephen M. Stigler, TTH, 1:30-2:50 PM, Kent 107.
PQ: MATH 19600, 20100, or 20400
Recommended Reading: Detailed notes will be distributed in the first quarter. The recommended (but not required in the first quarter) text is "Mathematical Statistics and Data Analysis (2nd edition, 1995)", by John A. Rice (Duxbury).

STATISTICS 30400. Distribution Theory.
Sec 01: Dan Nicolae, MWF, 1:30-2:20 PM, Eckhart 117.
PQ: STAT 24500 & MATH 25000 or equivalent
Recommended Reading: Severini, T. (2005). "Elements of Distribution Theory." Cambridge University Press.

This course covers the basics of distribution theory. Topics include:

Distribution functions and their inverses, quantile functions, Q/Q plots, change-of-variables for probability densities
Expectation, variance, median, mode of random variables
Basics of measure theory, including Fubini's theorem and interchangeability of limits and integrals
Moment generating functions and characteristic functions, including power series expansion, inversion formulas, uniqueness theorems, and convergence in distribution
Cumulants and cumulant generating functions: examples, properties
Different concepts of convergence for random variables
Limit theorems, including the weak law of large numbers, the central limit theorem.

STATISTICS 30700=CMSC 37800. Numerical Computation.
Sec 01: Robert Kirby, MWF, 11:30-12:20 PM, Ryerson 277.
PQ: Consent of instructor and elementary programming experience
Required Reading: "Numerical linear algebra" by Trefethen & Bau (SIAM Press)
Recommended Reading: "Applied linear algebra" by Demmel (SIAM Press)
"Matrix Computations" by Golub & van Loan, (3rd edition, Johns Hopkins Press).

This course covers numerical linear algebra, including matrix decompositions, direct and iterative methods for the solution of linear systems, and eigenvalue problems. Homework will involve a combination of mathematical and programming assignments.

STATISTICS 33100. Sample Surveys.
Sec 01: Kirk Wolter, TTH, 10:30-11:50 AM, Eckhart 117.
PQ: Consent of instructor
Reading: Cochran, W. G. (1977). "Sampling Techniques," 3rd edition. Wiley

This is an introductory course to the statistics and methodology of sample surveys. Topics include

basic methods of sample selection,
determining sample size, stratification,
general estimators (Horvitz-Thompson, ratio, generalized regression, calibration),
domain estimation,
nonresponse,
nonsampling error,
multiple-stage sampling,
a national sampling frame for area probability surveys,
telephone surveys,
questionnaire design,
variance estimation for complex surveys,
analysis of contingency tables, and
regression analysis for survey data.

It will be of interest to students who anticipate a research career that designs, collects, and analyzes survey data in fields such as economics, education, healthcare, marketing, psychology, sociology, and statistics.

STATISTICS 34300. Applied Linear Stat Methods.
Sec 01: Wei Biao Wu, TTH, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 24500 & MATH 25000 or equivalent
Required Reading: Venables, W.N. and Ripley, B.D. (1999). "Modern Applied Statistics with S-Plus (3rd ed)". Springer-Verlag.
Weisberg, S. (2005). "Applied Linear Regression, Third Edition". John Wiley & Sons.
Software: Splus or R.

STATISTICS 35000=HSTD 30900, ENST 27400, PPHA 36400. Principles of Epidemiology.
Sec 01: Kurina Lianne, TTH, 9:00-10:20 AM, BSLC 240.
PQ: Introductory Statistics
Required Reading: Gordis, Leon (2004). "Epidemiology, 3rd Ed". Elsevier Science.

STATISTICS 35900. Statistics in Neuroscience.
Sec 01: Yali Amit, TTH, 10:30-11:50 AM, Ryerson 277.
PQ: Consent of instructor
Required Reading: None.

This course will provide a survey of recent literature on the statistical analysis of neural recordings. Estimation of receptive fields, variations on reverse correlation, correlation analysis, decoding using Bayesian methods, time dependent models for analysis of spike train data. The course will involve student presentation of papers. Introductory lectures on the biological background and the relevant statistical tools will be provided.

STATISTICS 36900=HSTD 33300. Longitudinal Data Analysis.
Sec 01: Paul Rathouz, TTH, 9:00-10:20 AM, BSLC 313.
PQ: HSTD 32100/STAT 22200 or equiv and HSTD 32400/STAT 22400 or equiv OR consent of instructor
Required Reading: Diggle PJ, Heagerty P, Liang K-Y, & Zeger SL. (2002). "Analysis of Longitudinal Data, 2nd edn". Oxford: Oxford University Press.

Longitudinal data consist of multiple measures over time on a sample of individuals. This type
of data occurs extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Emphasis will be on data analysis and interpretation. Supporting statistical theory will be given at a level appropriate for an advanced Master’s student in Statistics. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies.

STATISTICS 37800. Statistical Computing.
Sec 01: Kenneth Wilder, MWF, 12:30-1:20 PM, Ryerson 277.
PQ: Consent of instructor
Required Reading: Accelerated C++, by Andrew Koenig and Barbara E. Moo

Students will be introduced to and gain experience in using a variety of computational tools that are useful for large statistical computing projects. These include R, python, C++, and various Unix utilities. The primary tool will be C++. The emphasis will be on choosing and being able to use the right tools for a project taking into account issues including< execution speed, memory usage, coding ease and portability.

Students will work on Linux operating systems, but no prior Linux/Unix experience is assumed.

STATISTICS 38200. Measure-Theoretic Probability-II (Probabilistic Aspects of Combinatorial Optimization).
Sec 01: Peter Radchenko, TTH, 3:00-4:20 PM, Eckhart 117.
PQ: Statistics 31300 or consent of instructor
Recommended Reading: “Probability theory and combinatorial optimization” by Michael Steele (CBMS-NSF regional conference series in applied mathematics, 1997).

This course is an introduction to the modern probability theory that is directly applicable to combinatorial optimization. We will consider simple stochastic models for the values of the problem inputs and concentrate on understanding the behavior of the objective function.

Applications will include:

Long-common-subsequence problem
Longest-increasing-subsequence problem
Traveling salesman problem
Minimal spanning trees
Steiner trees
Minimal matching problem
Nearest-neighbor-link problem

Probability topics will include:

Martingale inequalities
Subadditive sequences and functionals
Kingman’s subadditive ergodic theorem
Poissonization and de-Poissonization
Talagrand’s isoparametric theory of concentration inequalities

STATISTICS 39000=FINM 34500. Stochastic Calculus I.
Sec 01: Per Mykland, W 6:00-9:00 PM, Eckhart 202.
PQ: Enrollment in Mathematical Finance M. Sc. program or consent of instructor
Required Reading: Stochastic Calculus for Finance I-II, by Steven E. Shreve (required).
A Course in Financial Calculus, by Alison Etheridge (highly recommended).
`The basics of S-PLUS'', 3rd edition, by the same authors (A. Krause and M. Olson), ISBN 0-387-95456-2 (required).

This course is an introduction to stochastic calculus as it is relevant to the pricing and hedging of
options and other derivative securities. It is the first of a two-quarter sequence offered in collaboration
by the Department of Statistics and the master's program in Mathematical Finance. The main topics to
be covered are:

The Fundamental Theorem of Asset Pricing
Martingales
Brownian Motion
The Ito Integral and Ito's Formula
The Black-Sholes Formula
Girsanov's Formula
Currency Options
The Martingale Representation and Hedging

There will be weekly homework assignments, and midterm and final exams. The course assistants will conduct weekly help sessions on Friday afternoons.

SPRING 2005

STATISTICS 22200. Linear Models and Experimental Design.
Sec 01: Mei Wang, TTH, 1:30-2:50 PM, Eckhart 133.
Prerequisites: Statistics 22000 or equivalent.
Required Textbook: Oehlert, G. W. (2000) A First Course in Design and Analysis of Experiments. W. H. Freeman.
Optional Textbook: Hamilton, L. C. (2003) Statistics with Stata (Updated for Version 7). Duxbury Press.

This course will introduce the students to the major statistical issues in the design of experiments and the analysis of experimental data. The major topics will be

The basic principles of experimental design: randomization, blocking, and balance.
Important classes of designs: matched pairs, complete factorial designs, Latin squares, Graeco-Latin squares, fractional factorial designs and confounding.
Analysis of covariance and inference from experimental data.

In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment of their own.

STATISTICS 24600. Statistical Theory and Methods III: Computational Statistics.
Sec 01: Marc Coram, TTH, 10:30-11:50 AM, Eckhart 133.
Prerequisites: Statistics 24400 and 24500, or equivalent.
Recommended textbooks:
Main textbook:
Computational Statistics, Givens and Hoeting. Wiley 2005
Additional textbooks:
Gelman, A. Carlin, B., Stern, H. S., Rubin, D.B., Bayesian Data Analysis (2nd ed), Chapman & Hall, 2004.
Little, R. J. A., Rubin, D. B., Statistical Analysis with Missing Data (2nd ed), Wiley, 2002.
Rice, J. A., Matematical Statistics and Data Analysis (2nd ed), Duxbury Press, 1995.
Efron, B. and Tibshirani, R. J., An Introduction to the Bootstrap, Chapman & Hall, 1993.
Venables W. N., Ripley B. D., Modern Applied Statistics with S (4th ed), Springer, 1999.

This course is the third part of a sequence that introduces mathematical statistics and probability. In this quarter, the emphasis is on modern computer-intensive statistical methods.

The second part of the course presents an overview of the bootstrap and related nonparametric methods for assessing statistical accuracy.

Knowledge of probability distributions, random variables, and estimation techniques such as maximum likelihood from Statistics 24400 and 24500 is essential for this class.

STATISTICS 25100. Introduction to Mathematical Probability.
Sec 01: Michael Wichura, TTH, 3:00-4:20 PM, Eckhart 133.
Prerequisites: Math 20000 or 20300 (or equivalent) and completion of one of the Common Core sequences in the Biological or Physical Sciences, or permission from the instructor. No prior exposure to probability theory is required or assumed.
Textbook: Ross, Sheldon (2002), A First Course in Probability, Sixth Edition. Prentice-Hall.

STATISTICS 26700/36700, HIPS 25600, CHSS 32900. History of Statistics.
Sec 01: Stephen Stigler, TTH, 9:00-10:20 AM, Eckhart 133.
Prerequisites: A course in statistics.
Textbook: Stephen M. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900. (Cambridge, Mass.: Harvard University Press, 1986) (Available in paperback). Other materials will be distributed in class or by web.

STATISTICS 30200. Mathematical Statistics II.
Sec 01: Marc Coram, TTH, 3:00-4:20 PM, Eckhart 117.
Prerequisites: Statistics 30100.
Textbook: Casella and Berger: Statistical Inference.

This course continues the development of mathematical statistics. Topics of importance include: statistical decision theory, admissability and the Neyman-Pearson lemma, formal hypothesis testing, comparison with conditional frequentist and Bayesian viewpoints, ancillarity, interval estimation, UMP tests and MLR, unbiased tests, score statistics, generalized liklihood ratio tests and asymptotics, minimax estimation, empirical Bayes and "shrinkage"

STATISTICS 30800. Advanced Statistical Inference II.
Sec 01: Michael Stein, TTH, 1:30-2:50 PM, Eckhart 117.
Prerequisites: Statistics 30200 or equivalent.
Textbook: None.

The focus of the course will be on inference for parametric statistical models.

Autumn Quarter 2003

Statistics 22600: Analysis of Qualitative Data
Statistics 23400: Statistical Models and Methods I
Statistics 24400: Statistical Theory and Methods I
Statistics 30400: Distribution Theory
Statistics 33100: Sample Surveys
Statistics 33800: Statistical Inference for Financial Data
Statistics 34000: Applied Spatial Statistics
Statistics 34300: Applied Linear Statistical Methods
Statistics 37800: Statistical Computing
Statistics 38100: Measure-Theoretic Probability I
Statistics 39000: Stochastic Calculus I
Statistics 39400: Spectral Analysis of Time Series

Summer 2003

Statistics 39800: Field Work

Spring 2003

Statistics 24600: Statistical Theory and Methods III: Computational Statistics
Statistics 26700/36700=CFSC 32900, HIPS 25600: History of Statistics
Statistics 30200: Mathematical Statistics II
Statistics 31300: Intro to Stochastic Processes II
Statistics 35500: Statistical Genetics
Statistics 36100: Mathematics and Statistics for Neuroscience III
Statistics 38500: Advanced Topics in Probability
Statistics 39600: Nonparametric Statistics

Winter 2003

Statistics 22600: Analysis of Qualitative Data
Statistics 23500: Statistical Models and Methods II
Statistics 30100: Mathematical Statistics I
Statistics 31200: Intro to Stochastic Processes I
Statistics 34500: Design & Analysis of Experiments
Statistics 37100: Probability Bounds in Statistics
Statistics 38300: Measure-Theoretic Probability III
Statistics 39100=FINM 34600: Stochastic Calculus and Finance II

Autumn 2002

Statistics 23400: Statistical Models and Methods I
Statistics 30400: Distribution Theory
Statistics 30700*=CS37800: Numerical Computation
Statistics 33100: Sample Surveys
Statistics 34300: Applied Linear Stat Methods
Statistics 36500: Graphical Models in Multivariate Analysis
Statistics 37900: Topics in Computer Vision
Statistics 38100: Measure-Theoretic Probability I
Statistics 39000: Stochastic Calculus & Finance

Spring 2002

Statistics 30200: Mathematical Statistics II
Statistics 31300: Intro to Stochastic Processes II
Statistics 34700: Generalized Linear Models
Statistics 35500: Statistical Genetics
Statistics 36100: Statistics of Neural Spike Data
Statistics 36700=HIPSS256: History of Statistics
Statistics 37900=CS355: Topics in Computer Vision
Statistics 38500: Advanced Topics in Probability

Winter 2002

Statistics 22600: Analysis of Qualitative Data
Statistics 24700: Math/Stat Methods for Neuroscience II
Statistics 30100: Mathematical Statistics I
Statistics 31200: Introduction to Stochastic Processes I
Statistics 33200: Modeling Covariance and Correlated Data
Statistics 33300: State Space Models
Statistics 34500: Design & Analysis of Experiments
Statistics 38300: Measure-Theoretic Probability III
Statistics 38800: Topics in Discrete Probability
Statistics 39100=FINM34600: Stochastic Calculus & Finance II

Autumn 2001

Statistics 30400: Distribution Theory
Statistics 30700: Numerical Computation
Statistics 31400: Tensor Methods &Asymptotics
Statistics 33100: Sample Surveys
Statistics 34300: Applied Linear Stat Methods
Statistics 36200: Long Range Dependent Processes
Statistics 38100: Measure-Theoretic Probability I
Statistics 39000: Stochastic Calculus & Finance

Winter 2001

Statistics 30100: Mathematical Statistics I
Statistics 31200: Introduction to Stochastic Processes I
Statistics 32300: Bayesian Methods in Biostatistics
Statistics 34500: Design and Analysis of Experiments
Statistics 38300: Measure-Theoretical Probability III
Statistics 39100: Stochastic Calculus and Finance II
Statistics 47800: Topics in Computational Neural Science

Autumn 2000

Statistics 30400: Distribution Theory
Statistics 30700: Numerical Computation
Statistics 34300: Applied Linear Statistical Methods
Statistics 38100: Measure-Theoretical Probability I
Statistics 39000:Stochastic Calculus and Finance I

Spring 2000

Statistics 24600:Statistical Theory and Methods: Examples of Probability Models and Estimation
Statistics 30200:Mathematical Statistics II
Statistics 31300:Introduction to Stochastic Processes II
Statistics 34700:Generalized Linear Models
Statistics 26700-36700:History of Statistics
Statistics 37900:Topics in Computer Vision
Statistics 38500:Advanced Topics in Probability
Statistics 45500:Statistical Genetics

Winter 2000

Statistics 30100:Mathematical Statistics I
Statistics 31200:Introduction to Stochastic Processes I
Statistics 34500:Design and Analysis of Experiments
Statistics 34800:Advanced Data Analysis
Statistics 36400:Graphical Models
Statistics 38300:Measure-Theoretic Probability III
Statistics 38600:Topics in Stochastic Processes
Statistics 39100:Stochastic Calculus and Finance II
Statistics 45500:Statistical Genetics
Statistics 46300:Topics in Statistical Inference
Statistics 48500:Advanced Topics in Probability

Autumn 1999

Statistics 30400:Distribution Theory
Statistics 30700:Numerical Computation
Statistics 34300:Applied Linear Statistical Methods
Statistics 34900:Applied Non-Parametric Statistics
Statistics 37000:Algebraic Methods in Statistics
Statistics 38100:Measure-Theoretic Probability I

Spring 1999

Statistics 30200:Mathematical Statistics II
Statistics 32000:Bayesian Statistics
Statistics 45500:Introduction to Statistical Genetics
Statistics 26700-36700:History of Statistics

Winter 1999

Statistics30100:Mathematical Statistics
Statistics 31200:Introduction to Stochastic Processes
Statistics 34700:Generalized Linear Models
Statistics 34900:Applied Nonparametric Statistics
Statistics 38300:Measure-Theoretic Probability III
Statistics 39100:Stochastic Calculus and Finance II

Autumn 1998

Statistics 22000:Statistical Methods and their Applications
Statistics 30400:Applied Linear Statistical Methods
Statistics 34300:Distribution Theory
Statistics 39000:Stochastic Calculus and Finance I

Spring 1998

Statistics 22200: Linear Models and Experimental Design
Statistics 34700: Generalized Linear Models
Statistics 26700/36700: History of Statistics
Statistics 38500: Advanced Topics in Probability: Fundamentals of Ergodic Theory and Random Walk

Winter 1998

Statistics 30100/45800/45900: Mathematical Statistics I/Ia/Ib
Statistics 31300/41300/41400: Applied Probability II, IIa, IIb
Statistics 34500: Design and Analysis of Experiments
Statistics 45200: The Minimum Description Length Principle
Statistics 45500: Statistical Genetics
Statistics 49500: Nonparametric Regression

Autumn 1997

Statistics 30700: Numerical Computation
Statistics 25200/31200: Introduction to Stochastic Processes
Statistics 39000/Mathematical Finance 345: Stochastic Calculus and Finance
Statistics 59000: Stochastic Bootcamp (By appointment only)

Spring 1997

Statistics 33800: Statistical Shape Analysis
Statistics 34700: Generalized Linear Models
Statistics 36700: History of Statistics
Statistics 41500: Hidden Markov Models
Statistics 44200: A Case Study in Data Analysis
Statistics 45500: Statistical Genetics
Statistics 46100: Asymptotics in Inference
Statistics 46300: Topics in Statistical Inference
Statistics 47800: Statistical Algorithms
Statistics 49200: Wavelets

Winter 1997

Statistics 22600: Analysis of Qualitative Data
Statistics 24500: Statistical Theory and Methods II
Statistics 30900: Symbolic Computation
Statistics 31300: Applied Probability II
Statistics 34500: Design and Analysis of Experiments
Statistics 39500: Nonparametric Regression and Classification
Statistics 48800: Workshop on Shrinking Interval Asymptotics

Autumn 1996

Statistics 22700: Biostatistical Methods
Statistics 24400: Statistical Theory and Methods I
Statistics 30700: Numerical Computation
Statistics 31200: Introduction to Stochastic Processes
Statistics 33900: Spatial Statistics
Statistics 34300: Applied Linear Statistical Methods
Statistics 39000: Stochastic Calculus and Finance
Statistics 48800: Workshop on Shrinking Interval Asymptotics
Statistics 49700: The Craft of Research

Spring 1996

Statistics 22200: Linear Models and Experimental Design
Statistics 24000: Statistics and Probability for the Natural Sciences
Statistics 25100: Intro to Math Probability
Statistics 30200: Mathematical Statistics II
Statistics 32100: Applied Multivariate Analysis
Statistics 34700: Generalized Linear Models
Statistics 35300: Statistics of High Level Image Analysis and Computer Vision
Statistics 35500: Statistical Genetics
Statistics 36700: History of Statistics
Statistics 38200: Measure-Theoretic Probability II
Statistics 39000: Stochastic Calculus and Finance

Winter 1996

Statistics 22600: Analysis of Qualitative Data
Statistics 24500: Statistical Theory and Methods II
Statistics 30100: Mathematical Statistics I
Statistics 31400: Tensor Methods
Statistics 33200: Survey Topics
Statistics 34500: Design and Analysis of Experiments
Statistics 35900: Pedigree Analysis and Gene Mapping
Statistics 38100: Measure-Theoretic Probability I
Statistics 38900: Probability and Finance
Statistics 39500: Nonparametric Regression and Classification

Autumn 1995

Statistics 22400: Applied Regression Analysis
Statistics 22700: Biostatistical Methods
Statistics 24200: Applied Probability & Elementary Stochastic Models
Statistics 24400: Statistical Theory & Methods I
Statistics 30400: Distribution Theory
Statistics 31200: Introduction to Stochastic Processes
Statistics 32200: Bayesian Data Analysis
Statistics 33100: Sample Surveys
Statistics 34300: Applied Linear Statistical Methods
Statistics 35800: Mathematical Genetics: The Coalescent
Statistics 38300: Measure-Theoretic Probability III
Statistics 39400: Data Analysis with Spectral Methods and Their Modern Time-Frequency Counterparts

Spring 1995

Statistics 31600: Fourier Analysis with Application to Probability and Statistics
Statistics 33200: Survey Topics
Statistics 34700: Generalized Linear Models
Statistics 35300: Topics in High Level Image Analysis and Computer Vision
Statistics 35600: Mathematical Genetics
Statistics 36700: History of Statistics
Statistics 37700: Topics in Monte Carlo Simulation

Winter 1995

Statistics 32000: Bayesian Statistics
Statistics 32800: Biometry
Statistics 34500: Design and Analysis of Experiments
Statistics 35200: Speech Recognition
Statistics 36300: Topics in Likelihood Theory
Statistics 38100: Measure-Theoretic Prob I
Statistics 39000: Stochastic Calculus and Finance

Autumn 1994

Statistics 24400: Stat Theory and Methods I
Statistics 30100: Mathematical Statistics I
Statistics 31200: Intro to Stochastic Processes
Statistics 33100: Sample Surveys
Statistics 33500: Time Series
Statistics 34300: Appl Linear Statist Methods
Statistics 37500: Topics in Bayesian Analysis
Statistics 39200: Wavelets