Comparison of different computational implementations on fitting generalized linear mixed-effects models for repeated count measures

DOI10.1080/00949655.2015.1111376OpenAlexW2270799679MaRDI QIDQ5222487

Zhiwei Zhang, Lu Huang, Li Tang, Hui Zhang, Bo Zhang

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2015.1111376

zbMATH Keywords

linearizationoverdispersionSASintegral approximationrepeated count data

Mathematics Subject Classification ID

Statistics (62-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (2)

A semiparametric marginalized zero-inflated model for analyzing healthcare utilization panel data with missingness ⋮ A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Uses Software

• R
• S-PLUS
• lme4
• Stata
• SAS
• ADMB
• glmmML
• MCMCglmm
• AD Model Builder
• PROC NLMIXED
• MEMSS
• glmmAK
• FastGaussQuadrature
• glm
• pllf

Cites Work

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• Estimation in generalized linear models with random effects
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• The nonlinear mixed effects model with a smooth random effects density
• Maximum Likelihood Variance Components Estimation for Binary Data
• Population-averaged and subjectspecific approaches for clustered categorical data
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Mixed-Effects Models in S and S-PLUS
• Approximate Inference in Generalized Linear Mixed Models
• Mixed Models
• Generalized linear mixed models a pseudo-likelihood approach
• Modeling longitudinal binomial responses: implications from two dueling paradigms
• A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses
• Fast and Accurate Computation of Gauss--Legendre and Gauss--Jacobi Quadrature Nodes and Weights
• Modern Applied U‐Statistics
• Longitudinal Data Analysis

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