Sieve Estimation with Bivariate Interval Censored Data

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Xiaoli Gao, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Bivariate interval censored data arises in many applications. However, both theoreticaland computational investigations for this type of data are limited because of thecomplicated structure of bivariate censoring. In this paper, we propose a two-stage splinebasedsieve estimator for the association between two event times with bivariate case 2interval censored data. A smooth and explicit estimator for the joint distribution functionis also available. The proposed estimators are shown to be asymptotically consistent andcomputationally efficient. We demonstrate the finite sample performances of the splinebasedsieve estimators using both simulation studies and real data analysis from an AIDSclinical trial study.

Additional Information

Journal of Statistics: Advances in Theory and Applications
Language: English
Date: 2011
association parameter, bivariate interval censored data, case 2 interval censored data, copula model, semiparametric problem, spline-based sieve estimator

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