Joint hierarchical generalized linear models with multivariate Gaussian random effects

DOI10.1016/J.CSDA.2013.07.011zbMath1471.62141OpenAlexW1996781295MaRDI QIDQ1615152

Marek Molas, Youngjo Lee, Maengseok Noh, Emmanuel Lesaffre

Publication date: 2 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.2013.07.011

zbMATH Keywords

Newton-Raphson algorithmjoint model\(H\)-likelihoodmultivariate Gaussian random effectsrheumatoid arthritis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Uses Software

• HGLMMM

Cites Work

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• Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions
• Computing the nearest correlation matrix--a problem from finance
• Comparison of hierarchical and marginal likelihood estimators for binary outcomes
• On diagnostics in double generalized linear models
• REML estimation for binary data in GLMMs
• An extended quasi-likelihood function
• Variance Components Testing in the Longitudinal Mixed Effects Model
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Joint Modelling of Repeated Measures and Survival Time Data
• Generalized Linear Models with Random Effects
• Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles

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