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