文章基本信息

标题：Informative $g$-Priors for Logistic Regression
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作者：Timothy E. Hanson ; Adam J. Branscum ; Wesley O. Johnson 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2014
卷号：9
期号：3
页码：597-612
DOI：10.1214/14-BA868
语种：English
出版社：International Society for Bayesian Analysis
摘要：Eliciting information from experts for use in constructing prior distributions for logistic regression coefficients can be challenging. The task is especially difficult when the model contains many predictor variables, because the expert is asked to provide summary information about the probability of “success” for many subgroups of the population. Often, however, experts are confident only in their assessment of the population as a whole. This paper is about incorporating such overall information easily into a logistic regression data analysis using g-priors. We present a version of the g-prior such that the prior distribution on the overall population logistic regression probabilities of success can be set to match a beta distribution. A simple data augmentation formulation allows implementation in standard statistical software packages.