Journal
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
Volume 63, Issue 3, Pages 511-533Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10463-009-0239-z
Keywords
Association; Binary outcomes; Clustered data; Estimating equation; Missing at random; Semiparametric estimation
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- NIAD/NIH [AI62247, AI059773]
- NSF [DMS-0806097]
- National Heart, Lung, and Blood Institute (NHLBI)
- Boston University
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Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to inference about the mean response parameters with the association parameters relegated to a nuisance role. There is little work concerning both the marginal and association structures, especially in the semiparametric framework. In this paper, our interest centers on inference on the association parameters in addition to the mean parameters. We develop semiparametric methods for both complete and incomplete clustered binary data and establish the theoretical results. The proposed methodology is illustrated through numerical studies.
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