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

Autor:	Kazuaki Murayama, Kohei Yoshikawa, Kaito Shimamura, Shengyi Wu, Shuichi Kawano
Rok vydání:	2021
Předmět:	Fusion Computer science Bayesian probability Markov chain Monte Carlo Statistics::Computation Variable (computer science) symbols.namesake Physics::Plasma Physics Linear regression Prior probability symbols Bayesian linear regression Algorithm Gibbs sampling
Zdroj:	Intelligent Decision Technologies ISBN: 9789811627644 KES-IDT
DOI:	10.1007/978-981-16-2765-1_41
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5c024b6a9aca559fdd304ea918f4debe https://doi.org/10.1007/978-981-16-2765-1_41 Zobrazit plný text záznamu