The Annals of Statistics

A Heavy Censoring Limit Theorem for the Product Limit Estimator

Jon A. Wellner

Full-text: Open access

Abstract

A key identity for the product-limit estimator due to Aalen and Johansen (1978) and Gill (1980) is shown to be a consequence of the exponential formula of Doleans-Dade (1970). The basic counting processes in the censored data problem are shown to converge jointly to Poisson processes under "heavy-censoring": $G_n \rightarrow_d \delta_0$, but $n(1 - G_n) \rightarrow \alpha$ where $G_n$ is the censoring distribution. The Poisson limit theorem for counting processes implies Poisson type limit theorems under heavy censoring for the cumulative hazard function estimator and product limit estimator. The latter, in combination with the key identity of Aalen-Johansen and Gill and martingale properties of the limit processes, yields a new approximate variance formula for the product limit estimator which is compared numerically with recent finite sample calculations for the case of proportional hazard censoring due to Chen, Hollander, and Langberg (1982).

Article information

Source
Ann. Statist., Volume 13, Number 1 (1985), 150-162.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346583

Digital Object Identifier
doi:10.1214/aos/1176346583

Mathematical Reviews number (MathSciNet)
MR773159

Zentralblatt MATH identifier
0609.62061

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 60F05: Central limit and other weak theorems 62G30: Order statistics; empirical distribution functions 60G44: Martingales with continuous parameter

Keywords
Cumulative hazard function estimator exponential formula Kaplan-Meier estimator martingales Poisson process small sample moments variance formulas

Citation

Wellner, Jon A. A Heavy Censoring Limit Theorem for the Product Limit Estimator. Ann. Statist. 13 (1985), no. 1, 150--162. doi:10.1214/aos/1176346583. https://projecteuclid.org/euclid.aos/1176346583


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