Brad Efron has been at Stanford for more than fifty years and has represented the Statistics Department, as well as leading the Mathematical and Computational Science program, for the past 30. He 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.
"I had a very complicated idea called 'the combination distribution' for explaining the jackknife, but the more I worked on it the simpler it got, until I was left with what seemed like almost nothing at all. And that was the bootstrap."
He has made seminal contributions to many areas of statistics, and his thinking has influenced many scientific disciplines including medicine, physics, astronomy, biology, economics, sociology, and computer science. More importantly, Brad always makes statistics fun, engaging, and important. This day-long celebration of our friend and colleague will bring his former students and collaborators back to the Farm for an Efron-centric series of presentations to thank him for sharing with us his kindness, generosity, irreverent humor, and encouraging spirit.
|9:15a||Welcome – Rob Tibshirani and Trevor Hastie|
|9:30a||Arthur Peterson, Jr., University of Washington: "Censored data, randomized trials, and the teaching of statistics: Some contributions by Brad to science (and to me)"|
|10:00a||Gary Simon, New York University: "Gerrymandering: How bad is it?"|
|10:30a||Morning Coffee Break|
|10:45a||Ronald Thisted, University of Chicago: "Reproducing Shakespeare"|
|11:15a||Abhinanda Sarkar, Mysore Royal Academy: "Reflections on the Efron view of statistics in the modern world"|
|12:00n||Lunch @ Sequoia Hall, Jacaranda Courtyard|
|1:30p||Terry Therneau, Mayo Clinic: "Simple problems and unexpected answers"|
|2:00p||Samuel Kou, Harvard University: "Optimal shrinkage estimation in heteroscedastic hierarchical models: Empirical Bayes and beyond"|
|2:30p||Afternoon Coffee Break|
|3:00p||Omkar Muralidharan, Google: "Evaluating models for heterogeneous causal effects"|
|3:30p||Stefan Wager, Stanford University: "Bias-aware confidence intervals for empirical Bayes estimation"|