The central limit theorem under censoring

Michael G. Akritas

doi:10.2307/3318473

The central limit theorem under censoring

Michael G. Akritas

Statistics

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

The central limit theorem for integrals of the Kaplan-Meier estimator is obtained. The basic tools are the martingale methods developed by Gill and the identities and inequalities of Efron and Johnstone. The assumptions needed are both weaker and more transparent than those in the recent literature, and the resulting variance expression is simpler, especially for distributions with atoms.

Original language	English (US)
Pages (from-to)	1109-1120
Number of pages	12
Journal	Bernoulli
Volume	6
Issue number	6
DOIs	https://doi.org/10.2307/3318473
State	Published - 2000

All Science Journal Classification (ASJC) codes

Statistics and Probability

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The central limit theorem under censoring

Abstract

All Science Journal Classification (ASJC) codes

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