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February 1998 Efficient estimation from right-censored data when failure indicators are missing at random
Mark J. van der Laan, Ian W. McKeague
Ann. Statist. 26(1): 164-182 (February 1998). DOI: 10.1214/aos/1030563981

Abstract

The Kaplan-Meier estimator of a survival function is well known to be asymptotically efficient when cause of failure is always observed. It has been an open problem, however, to find an efficient estimator when failure indicators are missing at random. Lo showed that nonparametric maximum likelihood estimators are inconsistent, and this has led to several proposals of ad hoc estimators, none of which are efficient. We now introduce a sieved nonparametric maximum likelihood estimator, and show that it is efficient. Our approach is related to the estimation of a bivariate survival function from bivariate right-censored data.

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Mark J. van der Laan. Ian W. McKeague. "Efficient estimation from right-censored data when failure indicators are missing at random." Ann. Statist. 26 (1) 164 - 182, February 1998. https://doi.org/10.1214/aos/1030563981

Information

Published: February 1998
First available in Project Euclid: 28 August 2002

zbMATH: 0932.62040
MathSciNet: MR1611792
Digital Object Identifier: 10.1214/aos/1030563981

Subjects:
Primary: 62G07
Secondary: 62F12

Rights: Copyright © 1998 Institute of Mathematical Statistics

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Vol.26 • No. 1 • February 1998
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