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Activity Number: 434
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #310748
Title: Spike and Slab Variable Selection via EMVS
Author(s): Veronika Rockova*+ and Edward George
Companies: Wharton School and Wharton School
Keywords: EMVS ; Bayesian variable selection ; High-dimensional ; Spike and slab

EMVS is a fast deterministic approach to identifying sparse high posterior models for Bayesian variable selection under spike-and-slab priors. We consider the variable selection properties of the EMVS procedure under the unifying framework of regularized least squares with non-concave penalties. Optimal EMVS calibrations are derived that guarantee the recoverability of the true sparse model with probability tending to one. Heavy-tailed spike and slab formulations will be described that ameliorate the bias problems associated with Gaussian spike and slab priors. Motivated by the asymptotic arguments, we investigate the usefulness of determinantal variable selection priors, which provide prior support on recoverable subsets, penalize redundancy in model selection and thereby alleviate the multi-modality associated with collinear predictors. The presented methodology will be illustrated on a practical example of finding extreme non-additive effects in a high-dimensional ANOVA model.

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