This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 301
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #306681
Title: The NPMLE of the Joint Distribution Function with Right-Censored and Masked Competing Risks Data
Author(s): Qiqing Yu*+
Companies: Binghamton University
Address: 39 Green Meadow Lane, Binghamton, NY, 13901, USA
Keywords: Right-censorship ; competing risks model ; self-consistent algorithm ; generalized MLE ; consistency ; asymptotic normality

Even though the right-censored competing risks data with masked failure cause have been studied for 30 years, the asymptotic properties of the non-parametric maximum likelihood estimator (NPMLE) of the joint distribution function with such data have never been studied. The solution to the NPMLE is not unique and we show that the NPMLE studied in the current literature is inconsistent. Moreover, we propose a consistent NPMLE and establish its asymptotic normality. It is a nontrivial example in the survival analysis context that there exist an inconsistent NPMLE as well as a consistent NPMLE under the same model. Our proofs do not need the symmetry assumption made by almost all researchers on such data. We present simulation results on the consistent NPMLE and apply the NPMLE to a data set in medical research.

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