Open Access
June 2009 Consistency of Bayesian procedures for variable selection
George Casella, F. Javier Girón, M. Lina Martínez, Elías Moreno
Ann. Statist. 37(3): 1207-1228 (June 2009). DOI: 10.1214/08-AOS606


It has long been known that for the comparison of pairwise nested models, a decision based on the Bayes factor produces a consistent model selector (in the frequentist sense). Here we go beyond the usual consistency for nested pairwise models, and show that for a wide class of prior distributions, including intrinsic priors, the corresponding Bayesian procedure for variable selection in normal regression is consistent in the entire class of normal linear models. We find that the asymptotics of the Bayes factors for intrinsic priors are equivalent to those of the Schwarz (BIC) criterion. Also, recall that the Jeffreys–Lindley paradox refers to the well-known fact that a point null hypothesis on the normal mean parameter is always accepted when the variance of the conjugate prior goes to infinity. This implies that some limiting forms of proper prior distributions are not necessarily suitable for testing problems. Intrinsic priors are limits of proper prior distributions, and for finite sample sizes they have been proved to behave extremely well for variable selection in regression; a consequence of our results is that for intrinsic priors Lindley’s paradox does not arise.


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George Casella. F. Javier Girón. M. Lina Martínez. Elías Moreno. "Consistency of Bayesian procedures for variable selection." Ann. Statist. 37 (3) 1207 - 1228, June 2009.


Published: June 2009
First available in Project Euclid: 10 April 2009

zbMATH: 1160.62004
MathSciNet: MR2509072
Digital Object Identifier: 10.1214/08-AOS606

Primary: 62F05
Secondary: 62J15

Keywords: Bayes factors , consistency , intrinsic priors , linear models

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 3 • June 2009
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