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Activity Number: 304
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304624
Title: Bayes and Empirical-Bayes Multiplicity Adjustment in the Variable-Selection Problem
Author(s): James Scott*+
Companies: Duke University
Address: Box 90251, Durham, NC, 27708,
Keywords: multiple testing ; linear models ; empirical Bayes
Abstract:

In this talk, I will present a theorem that characterizes a surprising discrepancy between fully Bayes and empirical-Bayes approaches to multiplicity adjustment in linear regression. This discrepancy arises from a different source than the failure to account for uncertainty in the empirical-Bayes estimate, which is the usual issue in such problems. Indeed, I will show that even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true parameter value, the potential for a serious difference remains.

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