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

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2011年 / 55卷 / 01期

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

页码：703 / 714

页数：12

共 36 条

[1] Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies [J].