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The jackknife estimate of variance of a Kaplan-Meier integral. (English) Zbl 0878.62027

Summary: Let \(\widehat{F}_n\) be the Kaplan-Meier estimator of a distribution function \(F\) computed from randomly censored data. It is known that, under certain integrability assumptions on a function \(\varphi\), the Kaplan-Meier integral \(\int\varphi d\widehat{F}_n\), when properly standardized, is asymptotically normal. In this paper it is shown that, with probability 1, the jackknife estimate of variance consistently estimates the (limit) variance.

MSC:

62G05 Nonparametric estimation
62G20 Asymptotic properties of nonparametric inference
62G09 Nonparametric statistical resampling methods
62G30 Order statistics; empirical distribution functions
60G42 Martingales with discrete parameter

Software:

bootstrap
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References:

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