Current status data with competing risks: Consistency and rates of convergence of the MLE

P. Groeneboom; M.H. Maathuis; J.A. Wellner

doi:10.1214/009053607000000974

Current status data with competing risks: Consistency and rates of convergence of the MLE

P. Groeneboom, M.H. Maathuis, J.A. Wellner

Mathematics

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Abstract

We study nonparametric estimation of the sub-distribution functions for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider a simpler "naive estimator." Both types of estimators were studied by Jewell, van der Laan and Henneman [Biometrika (2003) 90 183-197], but little was known about their large sample properties. We have started to fill this gap, by proving that the estimators are consistent and converge globally and locally at rate n

Original language	English
Pages (from-to)	1031-1063
Number of pages	33
Journal	Annals of Statistics
Volume	36
Issue number	3
DOIs	https://doi.org/10.1214/009053607000000974
Publication status	Published - 2008

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AU - Wellner, J.A.

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AB - We study nonparametric estimation of the sub-distribution functions for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider a simpler "naive estimator." Both types of estimators were studied by Jewell, van der Laan and Henneman [Biometrika (2003) 90 183-197], but little was known about their large sample properties. We have started to fill this gap, by proving that the estimators are consistent and converge globally and locally at rate n

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DO - 10.1214/009053607000000974

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JO - Annals of Statistics

JF - Annals of Statistics

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Current status data with competing risks: Consistency and rates of convergence of the MLE

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