Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)

Bradley Efron

ISBN 10: 110761967X / ISBN 13: 9781107619678

Published by Cambridge University Press, 2012

About this Item

2012. Reprint. Paperback. Bradley Efron explains how to perform thousands of simultaneous estimates and tests, as required by new scientific technology. Series: Institute of Mathematical Statistics Monographs. Num Pages: 276 pages, 65 b/w illus. 10 colour illus. 105 exercises. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 225 x 154 x 14. Weight in Grams: 398. Empirical Bayes Methods for Estimation, Testing, and Prediction. Series: Institute of Mathematical Statistics Monographs. 280 pages, 65 b/w illus. 10 colour illus. 105 exercises. Bradley Efron explains how to perform thousands of simultaneous estimates and tests, as required by new scientific technology. Cateogry: (P) Professional & Vocational. BIC Classification: PBT. Dimension: 225 x 154 x 14. Weight: 442. . . . . .

Seller Inventory # V9781107619678

Bibliographic Details

• Title: Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)
• Publisher: Cambridge University Press
• Publication Date: 2012
• Language: English
• ISBN 10: 110761967X
• ISBN 13: 9781107619678
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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 using a large number of real examples.

About the Author

Bradley Efron is Max H. Stein Professor of Statistics and Biostatistics at the Stanford University School of Humanities and Sciences, and the Department of Health Research and Policy with the School of Medicine.