文章基本信息

标题：A Loss-Based Prior for Variable Selection in Linear Regression Methods
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作者：Cristiano Villa ; Jeong Eun Lee
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2020
卷号：15
期号：2
页码：533-558
DOI：10.1214/19-BA1162
语种：English
出版社：International Society for Bayesian Analysis
摘要：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.
关键词：Bayesian variable selection; linear regression; loss functions; objective priors