Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models

DOI10.1016/j.csda.2014.06.016zbMath1506.62103OpenAlexW2093476011MaRDI QIDQ1623700

Keunbaik Lee, Jae Keun Yoo

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.06.016

zbMATH Keywords

longitudinal dataheterogeneityCholesky decomposition

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Related Items

Bayesian cumulative logit random effects models with ARMA random effects covariance matrix, ARMA Cholesky factor models for the covariance matrix of linear models, Minimax estimation of a normal covariance matrix with the partial Iwasawa decomposition, Modeling of the ARMA random effects covariance matrix in logistic random effects models, Two-way ANOVA by using Cholesky decomposition and graphical representation

Uses Software

• BayesDA

Cites Work

• Unnamed Item
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• Approximate Inference in Generalized Linear Mixed Models
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
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• Monte Carlo sampling methods using Markov chains and their applications