The cluster bootstrap consistency in generalized estimating equations

Guang Cheng, Zhuqing Yu, Jianhua Z. Huang

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

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. © 2012.
Original languageEnglish (US)
Pages (from-to)33-47
Number of pages15
JournalJournal of Multivariate Analysis
Volume115
DOIs
StatePublished - Mar 2013
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: The first author's research was sponsored by NSF (DMS-0906497, CAREER Award DMS-1151692). The third author's research was partly sponsored by NSF (DMS-0907170), NCI (CA57030), and Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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