Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145847 | Journal of Multivariate Analysis | 2013 | 15 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Guang Cheng, Zhuqing Yu, Jianhua Z. Huang,