The Annals of Statistics

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

James G. Scott and James O. Berger

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This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham’s-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains.

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Ann. Statist. Volume 38, Number 5 (2010), 2587-2619.

First available in Project Euclid: 11 July 2010

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Mathematical Reviews number (MathSciNet)

Primary: 62J05: Linear regression 62J15: Paired and multiple comparisons

Bayesian model selection empirical Bayes multiple testing variable selection


Scott, James G.; Berger, James O. Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem. Ann. Statist. 38 (2010), no. 5, 2587--2619. doi:10.1214/10-AOS792.

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