TY - JOUR
T1 - The cluster bootstrap consistency in generalized estimating equations
AU - Cheng, Guang
AU - Yu, Zhuqing
AU - Huang, Jianhua Z.
N1 - 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.
PY - 2013/3
Y1 - 2013/3
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/599887
UR - https://linkinghub.elsevier.com/retrieve/pii/S0047259X12002175
UR - http://www.scopus.com/inward/record.url?scp=84868226541&partnerID=8YFLogxK
U2 - 10.1016/j.jmva.2012.09.003
DO - 10.1016/j.jmva.2012.09.003
M3 - Article
SN - 0047-259X
VL - 115
SP - 33
EP - 47
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
ER -