A comparison of Bayesian Markov chain Monte Carlo methods in a multilevel scenario

DOI10.1080/03610918.2021.1967985OpenAlexW3198356641MaRDI QIDQ6141691

Unnamed Author, Vimukthini Pinto, Roshini Sooriyarachchi

Publication date: 23 January 2024

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

Full work available at URL: https://doi.org/10.1080/03610918.2021.1967985

zbMATH Keywords

Markov chain Monte Carlogoodness-of-fitGibbs samplingMetropolis-Hastingsmultilevel modelingestimation techniques

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Longitudinal data analysis using generalized linear models
• Markov chain Monte Carlo: can we trust the third significant figure?
• Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design
• Rank-normalization, folding, and localization: an improved \(\widehat{R}\) for assessing convergence of MCMC (with Discussion)
• A Goodness of Fit Test for the Multilevel Logistic Model
• Practical Markov Chain Monte Carlo
• Goodness of fit tests for the multiple logistic regression model
• Goodness-of-Fit Tests for Ordinal Response Regression Models
• Applied Logistic Regression
• The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds
• A comparison of Bayesian and likelihood-based methods for fitting multilevel models

This page was built for publication: A comparison of Bayesian Markov chain Monte Carlo methods in a multilevel scenario