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A loss-based prior for variable selection in linear regression methods

Lookup NU author(s): Dr Cristiano VillaORCiD


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In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models, given the number of possible covariates under consideration. The worth of a model consists of the information loss and the loss due to model complexity. While the information loss is determined objectively, the loss expression due to model complexity is flexible and, the penalty on model size can be even customized to include some prior knowledge. Some versions of the loss-based prior are proposed and compared empirically. Through simulation studies and real data analyses, we compare the proposed prior to the Scott and Berger prior, for noninformative scenarios, and with the Beta-Binomial prior, for informative scenarios.

Publication metadata

Author(s): Villa C, Lee J

Publication type: Article

Publication status: Published

Journal: Bayesian Analysis

Year: 2019

Volume: 5

Issue: 2

Pages: 533-558

Print publication date: 01/05/2019

Acceptance date: 01/05/2019

ISSN (print): 1936-0975

ISSN (electronic): 1931-6690

Publisher: International Society for Bayesian Analysis


DOI: 10.1214/19-BA1162


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