Empirical Bayes Estimation of Random Effects Parameters in Mixed Effects Logistic Regression Models

DOI10.1111/j.0006-341X.1999.01022.xzbMath1059.62509WikidataQ52065197 ScholiaQ52065197MaRDI QIDQ4668299

Thomas R. Ten Have, A. Russell Localio

Publication date: 18 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

Bayesian estimationhierarchical modelspenalized quasi-likelihoodrandom slopeKass-Steffey numerical integration

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (7)

Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications ⋮ REML estimation for binary data in GLMMs ⋮ Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression ⋮ Testing for violations of the homogeneity needed for conditional logistic regression ⋮ A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED ⋮ Analysis of Clustered Binary Outcomes Using Within-Cluster Paired Resampling ⋮ Skew random effects in multilevel binomial models

Uses Software

• BUGS
• SAS/STAT

Cites Work

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• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Standard Errors of Prediction in Generalized Linear Mixed Models
• Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
• Latent Variable Models for Teratogenesis Using Multiple Binary Outcomes
• The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models
• Hierarchical Poisson Regression Modeling
• Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
• Approximate Inference in Generalized Linear Mixed Models
• A Random-Effects Ordinal Regression Model for Multilevel Analysis
• A note on the existence of the posterior distribution for a class of mixed models for binomial responses
• Generalized linear mixed models a pseudo-likelihood approach

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