Large-scale inference : empirical Bayes methods for estimation, testing, and prediction /

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Bibliographic Details
Author / Creator:	Efron, Bradley.
Imprint:	Cambridge ; New York : Cambridge University Press, ©2010.
Description:	1 online resource (xii, 263 pages) : illustrations
Language:	English
Series:	Institute of mathematical statistics monographs ; 1 Institute of Mathematical Statistics monographs ; 1.
Subject:	Bayesian statistical decision theory. Bayes Theorem. Statistics as Topic. Bayesian statistical decision theory.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11826645

Hidden Bibliographic Details
ISBN:	9780511918575 0511918577 9780511761362 0511761368 9786612818745 6612818743 9781107619678 110761967X 9780521192491 0521192498 9780511917592 0511917597 0511913001 9780511913006
Notes:	Includes bibliographical references and index. Print version record.
Summary:	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.
Other form:	Print version: Efron, Bradley. Large-scale inference. Cambridge : Cambridge University Press, 2010 9780521192491
Standard no.:	9786612818745

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