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

Efron, Bradley

ISBN 10: 0521192498 / ISBN 13: 9780521192491

Published by Cambridge University Press, 2010

About this Item

1st Edition (2010.) Hardcover without dust jacket issued. 8vo with 263 pages. The book is in very good condition very slight shelf wear. Interior clean and tight, No markings. No online access or CD-ROM or digital access codes if applicable! "I recommend that you read the book to understand the tests in more detail as well as the applications.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." Dark blue spine/White text. Size: 8vo.

Seller Inventory # 034131

Bibliographic Details

• Title: Large-Scale Inference : Empirical Bayes Methods for Estimation, Testing, and Prediction
• Publisher: Cambridge University Press
• Publication Date: 2010
• Language: English
• ISBN 10: 0521192498
• ISBN 13: 9780521192491
• Binding: Hardcover
• Edition: 1st Edition.
• Condition: Very Good
• Dust Jacket Condition: No DJ Issued
• Book Type: Probability & Statistics

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.