Abstract Details
Activity Number:
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538
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #309027 |
Title:
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Asymptotic Efficiency of Integral Estimators in the Semiparametric Random Censorship Model
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Author(s):
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Gerhard Dikta*+
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Companies:
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Fachhochschule Aachen
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Keywords:
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semi-parametric ;
information bound ;
asymptotically efficient ;
survival analysis ;
Kaplan-Meier estimator ;
random censorship model
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Abstract:
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We study the estimation of integrals of Borel-measurable functions with respect to some unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the integral estimator based on this semi-parametric estimator is at least as efficient as the corresponding nonparametric Kaplan-Meier integral estimator with respect to the asymptotic variance. In this paper we show that the semi-parametric integral estimator is asymptotically efficient with respect to the class of all regular, asymptotically linear estimators under SCRM.
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Authors who are presenting talks have a * after their name.
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