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Activity Number: 634 - Bayesian Methodology
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328682
Title: Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Author(s): Yabo Niu* and Nilabja Guha and Debkumar De and Anindya Bhadra and Veera Baladandayuthapani and Bani K. Mallick
Companies: Texas A&M University and University of Massachusetts Lowell and Texas A&M University and Purdue University and UT MD Anderson Cancer Center and Texas A&M University
Keywords: Bayesian variable selection; Decomposable graph; Gaussian graphical model; Hyper-inverse Wishart prior; Zellner's g-prior

Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorporate covariate information in this GGMs setup in a nonlinear seemingly unrelated regression framework. We propose a joint predictor and graph selection model and develop an efficient collapsed Gibbs sampler algorithm to search the joint model space. Furthermore, we investigate its theoretical model selection properties. We demonstrate our method on a variety of simulated data, concluding with a real data set from the TCPA project.

Authors who are presenting talks have a * after their name.

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