[Ml-stat-talks] Brad Efron, Wed 4/17, 5:30 PM, Friend 101

Ahmet Emre Barut abarut at Princeton.EDU
Wed Apr 10 08:11:16 EDT 2013

You are cordially invited to the S.S. Wilks Memorial Lecture on Wednesday, April 17 at 5:30pm in Friend 101.

A 250-Year Argument (Belief, Behavior, and the Bootstrap)
by Bradley Efron<http://www-stat.stanford.edu/~ckirby/brad/> (Stanford University)

The year 2013 marks the 250th anniversary of Bayes rule. The rule has been influential over the entire period, and controversial over most of it. It's reliance on prior beliefs has been challenged by frequentist methods, which focus instead on the behavior of specific estimates and tests. The bootstrap helps connect the two philosophies, particularly when Bayes inference is based on "uninformative" priors. Some examples will be used to illustrate the connection, without much in the way of theory.

Bradley Efron is the Max H. Stein Professor and Professor of Statistics and of Health Research and Policy at Stanford University. He is one of the world's most often-cited mathematical scientists and is best known for proposing the bootstrap resampling technique, which has had a major impact in the field of statistics and virtually every area of statistical application. He earned his doctorate in statistics from Stanford in 1964 and joined the Stanford faculty in 1965. Winner of a 1983 MacArthur Prize, he has served as president of the American Statistical Association and of the Institute of Mathematical Statistics. He has made many important contributions to many areas of statistics. Efron's work has spanned both theoretical and applied topics, including empirical Bayes analysis (with Carl Morris), applications of differential geometry to statistical inference, the analysis of survival data, and inference for microarray gene expression data. He is the author of a classic monograph, "The Jackknife, the Bootstrap and Other Resampling Plans" (1982) and has also co-authored (with R. Tibshirani) the text "An Introduction to the Bootstrap" (1994). In 2010, he published the monograph "Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction"

[cid:part1.04000802.05010008 at princeton.edu]
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