Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction

Institute of Mathematical Statistics Monographs

by Bradley Efron

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing, and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated

Genres: Textbooks, Mathematics, Artificial Intelligence

276 pages, Hardcover

First published August 5, 2010

Reviews

[David Bickel · Rating 5 out of 5]

Efron clearly explains important recent advances in estimating the local false discovery rate. He also contrasts the methodology with more conventional multiple comparison procedures.