Detecting random-effects model misspecification via coarsened data

Mixed effects models provide a suitable framework for statistical inference in a wide range of applications. The validity of likelihood inference for this class of models usually depends on the assumptions on random effects. We develop diagnostic tools for detecting random-effects model misspecification in a rich class of mixed effects models. These methods are illustrated via simulation and application to soybean growth data.

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Year of publication:	2011
Authors:	Huang, Xianzheng
Published in:	Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 55.2011, 1, p. 703-714
Publisher:	Elsevier
Keywords:	Generalized linear mixed models Kullback-Leibler divergence Nonlinear mixed models

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008864051