Modeling the random effects covariance matrix for generalized linear mixed models

DOI10.1016/j.csda.2011.09.011zbMath1243.62106OpenAlexW2056277434MaRDI QIDQ434920

Joseph Hagan, Keunbaik Lee, Jae Keun Yoo, Jung Bok Lee

Publication date: 16 July 2012

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

Full work available at URL: https://doi.org/10.1016/j.csda.2011.09.011

zbMATH Keywords

longitudinal dataheterogeneityCholesky decomposition

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (5)

Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models ⋮ Bayesian cumulative logit random effects models with ARMA random effects covariance matrix ⋮ ARMA Cholesky factor models for the covariance matrix of linear models ⋮ A marginalized multilevel model for bivariate longitudinal binary data ⋮ Modeling of the ARMA random effects covariance matrix in logistic random effects models

Uses Software

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Cites Work

• Analysis of zero-inflated clustered count data: a marginalized model approach
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• Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
• Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data
• Approximate Inference in Generalized Linear Mixed Models
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Regression models for covariance structures in longitudinal studies
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