Estimating negative variance components from Gaussian and non-Gaussian data: a mixed models approach

DOI10.1016/j.csda.2010.09.002zbMath1284.62060OpenAlexW1970711304MaRDI QIDQ2445816

Geert Molenberghs, Assam Pryseley, Clotaire Tchonlafi, Geert Verbeke

Publication date: 14 April 2014

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

Full work available at URL: https://doi.org/10.1016/j.csda.2010.09.002

zbMATH Keywords

marginal modellinear mixed modelgeneralized linear mixed modelrandom-effects modelGaussian and non-Gaussian datanegative variance component

Mathematics Subject Classification ID

Related Items (10)

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Cites Work

• Random-Effects Models for Longitudinal Data
• An extended random-effects approach to modeling repeated, overdispersed count data
• A pairwise likelihood approach to estimation in multilevel probit models
• Choice of units of analysis and modeling strategies in multilevel hierarchical models
• A hierarchical modeling approach for risk assessment in developmental toxicity studies
• A family of tests to detect misspecifications in the random-effects structure of generalized linear mixed models
• Misspecifying the likelihood for clustered binary data.
• Models for discrete longitudinal data.
• The Use of Score Tests for Inference on Variance Components
• Tests of Homogeneity for Generalized Linear Models
• Binary Regression Using an Extended Beta-Binomial Distribution, With Discussion of Correlation Induced by Covariate Measurement Errors
• 394: The Analysis of Binary Responses from Toxicological Experiments Involving Reproduction and Teratogenicity
• Tests to Detect Clustering of Infected Individuals within Families
• Approximate Inference in Generalized Linear Mixed Models
• Variance Components Testing in the Longitudinal Mixed Effects Model
• The interpretation of negative components of variance
• On the Distribution of the Likelihood Ratio
• On asymptotically optimal tests
• Linear mixed models for longitudinal data
• Unnamed Item
• Unnamed Item

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