Variable Fusion for Bayesian Linear Regression via Spike-and-slab Priors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies - Proceedings of the 13th KES-IDT 2021 Conference
EditorsIreneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages491-501
Number of pages11
ISBN (Print)9789811627644
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021 - Virtual, Online
Duration: Jun 14 2021Jun 16 2021

Publication series

NameSmart Innovation, Systems and Technologies
Volume238
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021
CityVirtual, Online
Period6/14/216/16/21

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Computer Science

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