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dc.contributor.authorCheng, Guang
dc.contributor.authorYu, Zhuqing
dc.contributor.authorHuang, Jianhua Z.
dc.date.accessioned2016-02-28T06:31:43Z
dc.date.available2016-02-28T06:31:43Z
dc.date.issued2013-03
dc.identifier.citationCheng G, Yu Z, Huang JZ (2013) The cluster bootstrap consistency in generalized estimating equations. Journal of Multivariate Analysis 115: 33–47. Available: http://dx.doi.org/10.1016/j.jmva.2012.09.003.
dc.identifier.issn0047-259X
dc.identifier.doi10.1016/j.jmva.2012.09.003
dc.identifier.urihttp://hdl.handle.net/10754/599887
dc.description.abstractThe 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.
dc.description.sponsorshipThe 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).
dc.publisherElsevier BV
dc.subjectBootstrap consistency
dc.subjectClustered/longitudinal data
dc.subjectExchangeably weighted cluster bootstrap
dc.subjectGeneralized estimating equations
dc.subjectOne-step bootstrap
dc.titleThe cluster bootstrap consistency in generalized estimating equations
dc.typeArticle
dc.identifier.journalJournal of Multivariate Analysis
dc.contributor.institutionPurdue University, West Lafayette, United States
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-CI-016-04


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