Bayesian analysis of joint mean and covariance models for longitudinal data

DOI10.1080/02664763.2014.920778OpenAlexW2031824325MaRDI QIDQ5130547

Zhong-Zhan Zhang, Deng-Ke Xu, Liu-Cang Wu

Publication date: 28 October 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2014.920778

zbMATH Keywords

Bayesian analysisGibbs samplerMetropolis-Hastings algorithmCholesky decompositionjoint mean and covariance models

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Longitudinal data analysis using generalized linear models
• A semiparametric Bayesian approach to joint mean and variance models
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• Bayesian inference in joint modelling of location and scale parameters of the \(t\) distribution for longitudinal data
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• A new approach to Cholesky-based covariance regularization in high dimensions
• Modelling of covariance structures in generalised estimating equations for longitudinal data
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• A moving average Cholesky factor model in covariance modelling for longitudinal data
• Practical Markov Chain Monte Carlo
• Bayesian Analysis of Semiparametric Mixed-Effects Models for Zero-Inflated Count Data
• On modelling mean-covariance structures in longitudinal studies
• Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
• Nonparametric Estimation of Covariance Structure in Longitudinal Data
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Regression models for covariance structures in longitudinal studies
• Bayesian modeling of variance heterogeneity in normal regression models

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