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 language | English (US) |
|---|---|
| Pages (from-to) | 33-47 |
| Number of pages | 15 |
| Journal | Journal of Multivariate Analysis |
| Volume | 115 |
| DOIs | |
| State | Published - Mar 2013 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged 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.