A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses
From MaRDI portal
Publication:5127070
DOI10.1080/02664763.2012.700452OpenAlexW2024341846MaRDI QIDQ5127070
Changyong Feng, Hui Zhang, Qin Yu, Pan Wu, Xinming Tu, Douglas Gunzler
Publication date: 21 October 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2012.700452
likelihood ratio testscore testgeneralized estimating equationsHotelling's \(T^2\) statisticgeneralized linear mixed-effect model
Related Items (5)
Statistical Implications of Endogeneity Induced by Residential Segregation in Small-Area Modeling of Health Inequities ⋮ Functional response models for intraclass correlation coefficients ⋮ Comparative GMM and GQL logistic regression models on hierarchical data ⋮ Comparison of different computational implementations on fitting generalized linear mixed-effects models for repeated count measures ⋮ A comparison study on modeling of clustered and overdispersed count data for multiple comparisons
Uses Software
Cites Work
- Longitudinal data analysis using generalized linear models
- Models for Longitudinal Data: A Generalized Estimating Equation Approach
- Correlated Binary Regression with Covariates Specific to Each Binary Observation
- Estimating Equations for Parameters in Means and Covariances of Multivariate Discrete and Continuous Responses
- Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data
- Inference for Kappas for Longitudinal Study Data: Applications to Sexual Health Research
- Inference and missing data
- The nonlinear mixed effects model with a smooth random effects density
- Dynamic Latent Trait Models for Multidimensional Longitudinal Data
- Mixed Models
- A Caveat Concerning Independence Estimating Equations with Multivariate Binary Data
- Bayesian latent variable models for mixed discrete outcomes
- Equivalence of conditional and marginal regression models for clustered and longitudinal data
- Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials
- Modern Applied U‐Statistics
- Recovery of inter-block information when block sizes are unequal
- Longitudinal Data Analysis
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses
