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Generalized linear models with clustered data: fixed and random effects modelsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2011 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 55, no 12, p. 3123-3134Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

2011. Vol. 55, no 12, p. 3123-3134
##### Keywords [en]

Bernoulli distribution, Gauss-Hermite quadrature, Laplace approximation, Implicit derivation, Profiling, Poisson distribution
##### National Category

Probability Theory and Statistics
##### Research subject

Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-40018DOI: 10.1016/j.csda.2011.06.011OAI: oai:DiVA.org:umu-40018DiVA, id: diva2:397338
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##### Funder

Riksbankens Jubileumsfond, 2005-0488Available from: 2011-02-14 Created: 2011-02-14 Last updated: 2018-06-08Bibliographically approved
##### In thesis

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.

1. Generalised linear models with clustered data$(function(){PrimeFaces.cw("OverlayPanel","overlay396958",{id:"formSmash:j_idt741:0:j_idt745",widgetVar:"overlay396958",target:"formSmash:j_idt741:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Generalized linear models with clustered data$(function(){PrimeFaces.cw("OverlayPanel","overlay507527",{id:"formSmash:j_idt741:1:j_idt745",widgetVar:"overlay507527",target:"formSmash:j_idt741:1:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

doi
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