Block Gibbs samplers for logistic mixed models: convergence properties and a comparison with full Gibbs samplers

DOI10.1214/21-EJS1930zbMath1493.62135arXiv2101.03849MaRDI QIDQ2074304

Yalin Rao, Vivekananda Roy

Publication date: 9 February 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.03849

zbMATH Keywords

geometric ergodicitydata augmentationMCMCstandard errorsdrift conditionGLMMMarkov chain CLT

Mathematics Subject Classification ID

Bayesian inference (62F15)

Uses Software

• DLMF

Cites Work

• The Pólya-gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic
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• Tailored randomized block MCMC methods with application to DSGE models
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• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
• Multivariate output analysis for Markov chain Monte Carlo
• Convergence Rates and Asymptotic Standard Errors for Markov Chain Monte Carlo Algorithms for Bayesian Probit Regression
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Bayesian auxiliary variable models for binary and multinomial regression
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