Detecting random-effects model misspecification via coarsened data

被引:18
作者
Huang, Xianzheng [1 ]
机构
[1] Univ S Carolina, Dept Stat, Columbia, SC 29208 USA
基金
美国国家科学基金会;
关键词
Generalized linear mixed models; Kullback-Leibler divergence; Nonlinear mixed models; GOODNESS-OF-FIT; LINEAR MIXED MODELS; TESTS; CONSEQUENCES; GEE;
D O I
10.1016/j.csda.2010.06.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:703 / 714
页数:12
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