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June 2004 Training samples in objective Bayesian model selection
James O. Berger, Luis R. Pericchi
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Ann. Statist. 32(3): 841-869 (June 2004). DOI: 10.1214/009053604000000229


Central to several objective approaches to Bayesian model selection is the use of training samples (subsets of the data), so as to allow utilization of improper objective priors. The most common prescription for choosing training samples is to choose them to be as small as possible, subject to yielding proper posteriors; these are called minimal training samples.

When data can vary widely in terms of either information content or impact on the improper priors, use of minimal training samples can be inadequate. Important examples include certain cases of discrete data, the presence of censored observations, and certain situations involving linear models and explanatory variables. Such situations require more sophisticated methods of choosing training samples. A variety of such methods are developed in this paper, and successfully applied in challenging situations.


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James O. Berger. Luis R. Pericchi. "Training samples in objective Bayesian model selection." Ann. Statist. 32 (3) 841 - 869, June 2004.


Published: June 2004
First available in Project Euclid: 24 May 2004

zbMATH: 1092.62034
MathSciNet: MR2065191
Digital Object Identifier: 10.1214/009053604000000229

Primary: 62F03 , 62F15
Secondary: 62B10 , 62F40 , 62N03

Keywords: Censored data , expected posterior priors , Intrinsic Bayes factors , intrinsic priors , linear models , objective priors , training samples

Rights: Copyright © 2004 Institute of Mathematical Statistics


Vol.32 • No. 3 • June 2004
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