Mixtures of g-priors for Bayesian Variable Selection

 

Zellner's g-prior remains a popular conventional prior for use in Bayesian variable selection, despite several well known consistency problems. In this talk, we study mixtures of g-priors as an alternative to default g-priors that resolve many of the problems with the original formulation, while maintaining the computational tractability that has made the g-prior so popular. We present theoretical properties of the mixture priors and provide real and simulated examples to compare the mixture formulation with fixed g-priors, Empirical Bayes approaches and other default procedures.