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Activity Number: 55 - Advances in Bayesian Sparse Regression
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313091
Title: Bayesian Hybrid Variable Selection Under Generalized Linear Models
Author(s): Shiqiang Jin* and Gyuhyeong Goh
Companies: Kansas State Univ and Kansas State University
Keywords: Bayesian model selection; High-dimensional generalized linear models; Laplace approximation; Stochastic variable search
Abstract:

In the era of Big Data, variable selection with high-dimensional data has drawn increasing attention. In this study, we propose a hybrid search algorithm to perform Bayesian high-dimensional variable selection under generalized linear models for various types of outcomes, including binary, count, and continuous data. Using Bayesian approximation techniques, we develop a novel computing strategy that enables us to evaluate all the marginal likelihoods of the neighborhood simultaneously in a single computation. In addition, to accelerate the convergence of our algorithm, we employ a hybrid search algorithm of deterministic local search and stochastic global search. Simulation studies and a real data example are shown to investigate the performance of the newly-developed method.


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