Detecting random-effects model misspecification via coarsened data |
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Authors: | Xianzheng Huang |
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Affiliation: | Department of Statistics, University of South Carolina, Columbia, SC 29208, USA |
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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. |
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Keywords: | Generalized linear mixed models Kullback-Leibler divergence Nonlinear mixed models |
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