A Bimodal Spike and Slab Model for Variable Selection and Model Exploration

Abstract

We have developed an enhanced spike and slab model for variable selection in linear regression models via restricted final prediction error (FPE) criteria; classic examples of which are AIC and BIC. Based on our proposed Bayesian hierarchical model, a Gibbs sampler is developed to sample models. The special structure of the prior enforces a unique mapping between sampling a model and calculating constrained ordinary least squares estimates for that model, which helps to formulate the restricted FPE criteria. Empirical comparisons are done to the lasso, adaptive lasso and relaxed lasso; followed by a real life data example.