Reversible jump MCMC to identify dropout mechanism in longitudinal data

DOI10.1080/03610926.2018.1472790OpenAlexW2899652015MaRDI QIDQ5083453

T. Baghfalaki, E. Farahani Jalali

Publication date: 20 June 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2018.1472790

zbMATH Keywords

longitudinal datareversible jump MCMCmarginal modelBayesian approachdropout mechanism

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Bayesian model choice in cumulative link ordinal regression models
• On Bayesian model and variable selection using MCMC
• Estimating the dimension of a model
• Information criteria and statistical modeling.
• Inference and missing data
• Informative Drop-Out in Longitudinal Data Analysis
• Bayes Factors
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• The Deviance Information Criterion: 12 Years on
• A new look at the statistical model identification

This page was built for publication: Reversible jump MCMC to identify dropout mechanism in longitudinal data