The cluster bootstrap consistency in generalized estimating equations

被引:41
作者
Cheng, Guang [1 ]
Yu, Zhuqing [1 ]
Huang, Jianhua Z. [2 ]
机构
[1] Purdue Univ, W Lafayette, IN 47907 USA
[2] Texas A&M Univ, College Stn, TX 77843 USA
基金
美国国家科学基金会;
关键词
Bootstrap consistency; Clustered/longitudinal data; Exchangeably weighted cluster bootstrap; Generalized estimating equations; One-step bootstrap; LINEAR-MODELS; LONGITUDINAL DATA;
D O I
10.1016/j.jmva.2012.09.003
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The cluster bootstrap resamples clusters or subjects instead of individual observations in order to preserve the dependence within each cluster or subject. In this paper, we provide a theoretical justification of using the cluster bootstrap for the inferences of the generalized estimating equations (GEE) for clustered/longitudinal data. Under the general exchangeable bootstrap weights, we show that the cluster bootstrap yields a consistent approximation of the distribution of the regression estimate, and a consistent approximation of the confidence sets. We also show that a computationally more efficient one-step version of the cluster bootstrap provides asymptotically equivalent inference. Published by Elsevier Inc.
引用
收藏
页码:33 / 47
页数:15
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