Efficient Estimation Methods for Informative Cluster Size Data
Date Issued
2005
Date
2005
Author(s)
Lee, Kuang-Yao
DOI
en-US
Abstract
In this study, we propose two estimation methods for considered marginal models under the cluster data setting with informative cluster size. The information of within-cluster correlation is appropriately used through the minimum cluster size in our approach, which is not fully considered in the within-cluster resampling (WCR) and cluster-weighted generalized estimating equation (CWGEE) methods.
It is known in the former works that the approaches of WCR and CWGEE are asymptotically equivalent but the WCR estimation procedure is computationally intensive.
When the within-cluster correlation is available and the minimum cluster size is greater than one, our estimatiors improve the inefficiency of the both estimators.
The finite sample properties of the proposed estimators are examined through a Monte Carlo simulation. Meanwhile, a comparison with the CWGEE method is made in the numerical study.
Subjects
群加權廣義估計式
群內重覆抽樣
非獨立群規模
within-cluster resampling
informative cluster size
cluster-weighted generalized estimating equation
Type
thesis
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