- Bayesian Anal.
- Advance publication (2018), 26 pages.
A Loss-Based Prior for Variable Selection in Linear Regression Methods
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.
Bayesian Anal., Advance publication (2018), 26 pages.
First available in Project Euclid: 14 June 2019
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Digital Object Identifier
Villa, Cristiano; Lee, Jeong Eun. A Loss-Based Prior for Variable Selection in Linear Regression Methods. Bayesian Anal., advance publication, 14 June 2019. doi:10.1214/19-BA1162. https://projecteuclid.org/euclid.ba/1560477728
- A loss-based prior for variable selection in linear regression methods. Supplementary Material. The Supplementary Material of “A loss-based prior for variable selection in linear regression” contains the Appendices A, B and C.