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 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
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