Variable fusion for Bayesian linear regression via spike-and-slab priors
Methodology
2021-11-22 v3 Machine Learning
Abstract
In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents a novel variable fusion method in terms of Bayesian linear regression models. We focus on hierarchical Bayesian models based on a spike-and-slab prior approach. A spike-and-slab prior is tailored to perform variable fusion. To obtain estimates of the parameters, we develop a Gibbs sampler for the parameters. Simulation studies and a real data analysis show that our proposed method achieves better performance than previous methods.
Cite
@article{arxiv.2003.13299,
title = {Variable fusion for Bayesian linear regression via spike-and-slab priors},
author = {Shengyi Wu and Kaito Shimamura and Kohei Yoshikawa and Kazuaki Murayama and Shuichi Kawano},
journal= {arXiv preprint arXiv:2003.13299},
year = {2021}
}
Comments
19 pages