JSM 2005 Online Program

Abstract #304391

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	362
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #304391
Title:	Nonparametric Estimation for Competing-risk, Current-status Data: Convex Minorant Characterizations and Algorithms
Author(s):	Marloes Maathuis*+
Companies:	University of Washington
Address:	University of Washington, Seattle, WA, 98195-4322, United States
Keywords:	current status ; competing risk ; maximum likelihood
Abstract:	We study nonparametric estimation of the subdistribution functions for competing-risk, current-status data. In particular, we consider the nonparametric maximum likelihood estimator (MLE) and the "naive estimator" proposed by Jewell, Van der Laan, and Henneman (2003). We present a new characterization for the MLE and use it to derive several self-induced convex minorant characterizations. These characterizations provide insight into the relationship between the MLE and the naive estimator and lead to iterative convex minorant algorithms for the computation of the MLE. We discuss one such algorithm in detail and prove its convergence. Finally, we compare the MLE and several variants of the naive estimator in a simulation study and show that the MLE is superior to the naive estimators in terms of mean-squared error.

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