A Brief History
The Department of Statistics of the University was established in
1949 to conduct research into advanced statistics and probability, to
work with others in the application of statistics to investigations
in the natural and social sciences, and to teach probability and statistical
theory and practice on the undergraduate and graduate levels.
From its beginning, the Department has been recognized for the high
quality of its faculty and the diversity of its interests. Some of the
most important and influential texts and monographs in statistics and
probability of the past forty years have been authored by former faculty
members of our Department: these include Ergodic Theory and Information,
Convergence of Probability Measures, and Probability and Measure, by
Patrick Billingsley; Inference and Disputed Authorship: The Federalist,
an application of Bayesian methods to fix the authorship of the Federalist
Papers, by David L. Wallace and Frederick Mosteller; and The Foundations
of Statistics, a famous analysis of fundamental problems by Leonard
J. Savage. Current members of our faculty have written definitive works
in a variety of areas of current research interest: these include Generalized
Linear Models, an influential monograph that extends the scope of linear
models greatly, including to models for discrete data, by Peter McCullagh
and John Nelder; Tensor Methods in Statistics, a monograph on methods
for making complex multivariate calculations, by Peter McCullagh; Elements
of Statistical Computing: Numerical Computation, a far-ranging text
on numerical methods for statistics by Ronald A. Thisted; The History
of Statistics: The Measurement of Uncertainty Before 1900, and Statistics
on the Table, accounts by Stephen M. Stigler of the historical development
of the field of mathematical statistics; Interpolation of Spatial Data:
Some Theory for Kriging, a monograph providing a sound mathematical
basis for understanding the behavior of a popular methodology for prediction
of spatial processes by Michael L. Stein; Michael J. Wichura recently
published a fundamental graduate text, The Coordinate Free Approach
to Linear Models; Lars Peter Hansen (with Thomas Sargent) recently published
Robustness, an adaptation of robust control techniques to mis-specification
problems in economics; Kirk Wolter's 2nd edition of his classic Introduction
to Variance Estimation was recently issued; and 2D Object Detection
and Recognition, Models, Algorithms, and Networks , a state-of-the-art
account of statistical methods in computer vision by Yali Amit.
Faculty members have contributed many statistical articles to books
and journals in theoretical and applied statistics, biophysics, chemistry,
mathematics, geophysics, astronomy, bacteriology, biometry, public health,
computer science, imaging, psychology, sociology, medicine, law, history
of science, education, and business. Members of the department have
at various times edited the four leading American or international journals
of probability and statistics (Greg Lawler is currently the Editor of
the Annals of Probability, the foremost research journal in the theory
of probability), and several have been president of one or both of the
two leading societies. Peter McCullagh, a leader in the development
of generalized linear models, is a Fellow of the Royal Society. Stephen
Stigler served recently as the President of the International Statistical
Institute. Michael Stein, is a leading expert in spatial and environmental
statistics. Per Mykland uses his expertise in martingale theory and
stochastic calculus to better understand financial markets. Yali Amit
is developing fundamentally new approaches to object recognition and
computer vision. Mary Sara McPeek and Matthew Stephens are world leaders
in statistical genetics.
Stephen M. Stigler
Chairman
|
