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

Shengyi Wu; Kaito Shimamura; Kohei Yoshikawa; Kazuaki Murayama; Shuichi Kawano

doi:10.1007/978-981-16-2765-1_41

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arXiv · 2003.13299

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

Shengyi Wu·Kaito Shimamura·Kohei Yoshikawa·Kazuaki Murayama·Shuichi Kawano

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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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Shengyi Wu, Kaito Shimamura, Kohei Yoshikawa, Kazuaki Murayama, Shuichi Kawano. 2020-03-30. Variable fusion for Bayesian linear regression via spike-and-slab priors. https://doi.org/10.1007/978-981-16-2765-1_41

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