The Annals of Statistics

Local Convergence of Empirical Measures in the Random Censorship Situation with Application to Density and Rate Estimators

Helmut Schafer

Full-text: Open access

Abstract

In this paper, we study the local deviations of the empirical measure defined by the Kaplan-Meier (1958) estimator for the survival function. The results are applied to derive best rates of convergence for kernel estimators for the density and hazard rate function in the random censorship model.

Article information

Source
Ann. Statist., Volume 14, Number 3 (1986), 1240-1245.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176350063

Digital Object Identifier
doi:10.1214/aos/1176350063

Mathematical Reviews number (MathSciNet)
MR856819

Zentralblatt MATH identifier
0612.62058

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62P10: Applications to biology and medical sciences

Keywords
Random censorship model empirical measures convergence rates kernel density estimation sample-point-dependent bandwidths

Citation

Schafer, Helmut. Local Convergence of Empirical Measures in the Random Censorship Situation with Application to Density and Rate Estimators. Ann. Statist. 14 (1986), no. 3, 1240--1245. doi:10.1214/aos/1176350063. https://projecteuclid.org/euclid.aos/1176350063


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