Title: Statistical inference in generalized linear mixed models: a review
Authors: Tuerlinckx, Francis ×
Rijmen, Frank
Verbeke, Geert
De Boeck, Paul #
Issue Date: Nov-2006
Publisher: J. Wright & Sons
Series Title: British Journal of Mathematical and Statistical Psychology vol:59 pages:225-255
Abstract: We present a review of statistical inference in generalized linear mixed models (GLMMs). GLMMs are an extension of generalized linear models and are suitable for the analysis of non-normal data with a clustered structure. A GLMM contains parameters common to all clusters (fixed regression effects and variance components) and cluster-specific parameters. The latter parameters are assumed to be randomly drawn from a population distribution. The parameters of this population distribution (the variance components) have to be estimated together with the fixed effects. We focus on the case in which the cluster-specific parameters are normally distributed. The cluster-specific effects are integrated out of the likelihood so that the fixed effects and variance components can be estimated. Unfortunately, the integral over the cluster-specific effects is intractable for most GLMMs with a normal mixing distribution. Within a classical statistical framework, we distinguish between two broad classes of methods to handle this intractable integral: methods that rely on a numerical approximation to the integral and methods that use an analytical approximation to the integrand. Finally, we present an overview of available methods for testing hypotheses about the parameters of GLMMs.
ISSN: 0007-1102
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
Onderzoeksgroep hogere cognitie en individuele verschillen (-)
× corresponding author
# (joint) last author

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