Use of between-within degrees of freedom as an alternative to the Kenward–Roger method for small-sample inference in generalized linear mixed modeling of clustered count data

DOI10.1080/03610918.2021.1982976OpenAlexW3203247192MaRDI QIDQ6141716

Unnamed Author, Vincent S. Staggs

Publication date: 23 January 2024

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2021.1982976

zbMATH Keywords

hierarchical modelclustered datamixed modelmultilevel modelgeneralized linear mixed model

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• An improved approximation to the precision of fixed effects from restricted maximum likelihood
• Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures
• Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
• Mixed Models
• Small sample estimation properties of longitudinal count models
• Comparison of naïve, Kenward–Roger, and parametric bootstrap interval approaches to small-sample inference in linear mixed models

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