Detecting random-effects model misspecification via coarsened data

Xianzheng Huang

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 703-714

Abstract: 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.

Keywords: Generalized; linear; mixed; models; Kullback-Leibler; divergence; Nonlinear; mixed; models (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:703-714

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