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Detecting random-effects model misspecification via coarsened data

Detecting random-effects model misspecification via coarsened data,10.1016/j.csda.2010.06.012,Computational Statistics & Data Analysis,Xianzheng Huang

Detecting random-effects model misspecification via coarsened data   (Citations: 2)
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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.
Journal: Computational Statistics & Data Analysis - CS&DA , vol. 55, no. 1, pp. 703-714, 2011
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