Bayesian Variable Selection with Shrinking and Diffusing Priors

• Activity Number: 388
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Naveen Naidu Narisetty*+ and Xuming He
• Companies: University of Michigan and University of Michigan
• Keywords: Bayes factor ; hierarchical model ; high dimensional data ; shrinkage ; variable selection

Abstract

We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the well-known spike and slab Gaussian priors with a distinct feature, that is, the prior variances depend on the sample size through which appropriate shrinkage can be achieved. We show the strong selection consistency of the proposed method in the sense that the posterior probability of the true model converges to one even when the number of covariates grows nearly exponentially with the sample size. This is arguably the strongest selection consistency result that has been available in the Bayesian variable selection literature; yet the proposed method can be carried out through posterior sampling with a simple Gibbs sampler. Furthermore, we argue that the proposed method is asymptotically similar to model selection with the L0 penalty. We also demonstrate through empirical work the fine performance of the proposed approach relative to some state of the art alternatives.