Generalized linear models with clustered data: fixed and random effects models
2011 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, Vol. 55, no 12, 3123-3134 p.Article in journal (Refereed) Published
The statistical analysis of mixed effects models for binary and count data is investigated. In the statistical computing environment R, there are a few packages that estimate models of this kind. The packagelme4 is a de facto standard for mixed effects models. The packageglmmML allows non-normal distributions in the specification of random intercepts. It also allows for the estimation of a fixed effects model, assuming that all cluster intercepts are distinct fixed parameters; moreover, a bootstrapping technique is implemented to replace asymptotic analysis. The random intercepts model is fitted using a maximum likelihood estimator with adaptive Gauss–Hermite and Laplace quadrature approximations of the likelihood function. The fixed effects model is fitted through a profiling approach, which is necessary when the number of clusters is large. In a simulation study, the two approaches are compared. The fixed effects model has severe bias when the mixed effects variance is positive and the number of clusters is large.
Place, publisher, year, edition, pages
2011. Vol. 55, no 12, 3123-3134 p.
Bernoulli distribution, Gauss-Hermite quadrature, Laplace approximation, Implicit derivation, Profiling, Poisson distribution
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-40018DOI: 10.1016/j.csda.2011.06.011OAI: oai:DiVA.org:umu-40018DiVA: diva2:397338
FunderRiksbankens Jubileumsfond, 2005-0488