Consistency of Bayesian procedures for variable selection

By George Casella, Javier Girón, Lina Martínez, Elias Moreno. 2009

148 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b3f8c18fb9c961618bd90d440f1a20175827e418

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Citations in the corpus

A variational Bayes approach to variable selection (2017, result): “Result (ii) is in keeping with consistency results of Bayesian inferential procedures (Casella et al., 2009).”
Criteria for Bayesian model choice with application to variable selection (2012, background): “A number of recent references concerning this criterion are Fernández, Ley and Steel (2001), Berger, Ghosh and Mukhopadhyay (2003), Liang et al. (2008), Casella et al. (2009), Guo and Speckman (2009).”