A1124
Title: Robust Bayesian elastic net with spike-and-slab priors
Authors: Xi Lu - University of Houston (United States) [presenting]
Abstract: In high-dimensional regression problems, the demand for robust variable selection arises due to the commonly observed outliers and heavy-tailed distributions of the response variable, as well as model misspecifications when structured sparsity is ignored. The robust elastic net in both the frequentist and Bayesian frameworks has received much attention in recent years for the robust identification of important omics features. A robust Bayesian elastic net with spike-and-slab priors is proposed, which overcomes the major limitations of the existing family of elastic net methods. Specifically, a fully Bayesian method is developed that builds on the robust likelihood function to safeguard against the heterogeneity of complex diseases while accounting for structured sparsity. Incorporation of the spike-and-slab priors in the Bayesian hierarchical model has significantly improved accuracy in shrinkage estimation and variable selection. The advantages of the proposed method have been demonstrated through the simulation study of data with independent and identically distributed random errors and heterogeneous random errors over multiple versions of elastic net regularization methods and other alternatives. The analysis of SNP data with strong LDs from the Nurse Health Study (NHS) has also revealed the superiority of the proposed method.
