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Abstract Details
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Activity Number:
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616
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #302410 |
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Title:
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Maximum Likelihood Estimation for Semiparametric Transformation Models
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Author(s):
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Sangbum Choi*+
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Companies:
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The University of Texas MD Anderson Cancer Center
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Address:
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1515 Holcombe Boulevard, Houston, TX, 77054,
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Keywords:
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Nonproportional hazards ;
Nonparametric likelihood ;
Counting process ;
Martingale residual ;
Survival analysis
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Abstract:
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Considerable interest has focused on efficient estimation for a class of semiparametric transformation models with censored observations. Chen (2009) and Zeng and Lin (2006) proposed efficient methods of estimating semiparametric transformation models, in which an underlying distribution function is completely known. This distribution in the frailty model context, however, may account for dependency among subjects or proportion of long-term survivors. Thus it is desirable to leave it specified up to a finite-dimensional parameter. In this paper, we present a simple modification of Chen's estimation procedure to estimate a distribution parameter as well as regression and function parameters. Direct maximization and profile likelihood approach are considered for nonparametric maximum likelihood estimation. Numerical study shows that the proposed methods perform well with practical sample sizes.
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Authors who are presenting talks have a * after their name.
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