Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Methodology
2021-08-03 v2 Statistics Theory
Statistics Theory
Abstract
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 variable selection properties. We demonstrate our method on a variety of simulated data, concluding with a real data set from the TCPA project.
Keywords
Cite
@article{arxiv.2010.14638,
title = {Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures},
author = {Yabo Niu and Nilabja Guha and Debkumar De and Anindya Bhadra and Veerabhadran Baladandayuthapani and Bani K. Mallick},
journal= {arXiv preprint arXiv:2010.14638},
year = {2021}
}