Variable fusion for Bayesian linear regression via spike-and-slab priors

• URL: http://arxiv.org/abs/2003.13299v3
• Date: Wed, 2 Dec 2020 09:06:39 GMT
• Title: Variable fusion for Bayesian linear regression via spike-and-slab priors
• Authors: Shengyi Wu, Kaito Shimamura, Kohei Yoshikawa, Kazuaki Murayama, Shuichi Kawano
• Abstract summary: This paper presents a novel variable fusion method in terms of Bayesian linear regression models. A spike-and-slab prior is tailored to perform variable fusion. Simulation studies and a real data analysis show that our proposed method achieves better performance than previous methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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