Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

DOI10.1080/03610918.2017.1341525OpenAlexW2625273810MaRDI QIDQ5085021

Miao-Yu Tsai, Mei-Hsien Lee, Jia-Chiun Pan

Publication date: 29 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2017.1341525

zbMATH Keywords

reversible jump Markov chain Monte CarloBayesian variable selectionstochastic search variable selectionlogistic mixed modelHolmes and Held algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• GeneSrF

Cites Work

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• The Bayesian Lasso
• On scale mixtures of normal distributions
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• Variable selection in Bayesian generalized linear‐mixed models: An illustration using candidate gene case‐control association studies
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Fixed and Random Effects Selection in Linear and Logistic Models
• Bayesian auxiliary variable models for binary and multinomial regression

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