JSM 2004 Online Program

Abstract #300175

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Activity Number:	169
Type:	Invited
Date/Time:	Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
Sponsor:	WNAR
Abstract - #300175
Title:	Maximum Likelihood Estimation in Transformation Models for Counting Processes
Author(s):	Danyu Lin*+ and Donglin Zeng
Companies:	University of North Carolina and University of North Carolina
Address:	, Chapel Hill, NC, 27599,
Keywords:	censoring ; intensity function ; multivariate failure times ; recurrent events ; semiparametric efficiency ; survival analysis
Abstract:	We propose a class of semiparametric transformation models with random effects for the regression analysis of counting processes with censored observations. We establish that the nonparametric maximum likelihood estimators for the parameters of interest are consistent and asymptotically Gaussian. Furthermore, the limiting variances achieve the semiparametric efficiency bounds and can be consistently estimated. Simulation studies show that the likelihood-based inference procedures perform well in practical settings. Illustration with a real medical study is provided.

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