Information consistency of the Jeffreys power-expected-posterior prior in Gaussian linear models

Dimitris Fouskakis () and Ioannis Ntzoufras ()
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Dimitris Fouskakis: National Technical University of Athens
Ioannis Ntzoufras: Athens University of Economics and Business

METRON, 2017, vol. 75, issue 3, No 11, 380 pages

Abstract: Abstract Power-expected-posterior (PEP) priors have been recently introduced as generalized versions of the expected-posterior-priors (EPPs) for variable selection in Gaussian linear models. They are minimally-informative priors that reduce the effect of training samples under the EPP approach, by combining ideas from the power-prior and unit-information-prior methodologies. In this paper we prove the information consistency of the PEP methodology, when using the independence Jeffreys as a baseline prior, for the variable selection problem in normal linear models.

Keywords: Bayes factors; Bayesian variable selection; Expected-posterior priors; Gaussian linear models; Imaginary training samples; Information consistency; Objective model selection methods; Power-expected-posterior priors (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s40300-017-0110-6

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