The Annals of Statistics

The Estimation of the Hazard Function from Randomly Censored Data by the Kernel Method

Martin A. Tanner and Wing Hung Wong

Full-text: Open access

Abstract

By convolution smoothing of the empirical hazards, a kernel estimate of the hazard function from censored data is obtained. Small and large sample expressions for the mean and the variance of the estimator are given. Conditions for asymptotic normality are investigated using the Hajek projection method.

Article information

Source
Ann. Statist., Volume 11, Number 3 (1983), 989-993.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346265

Digital Object Identifier
doi:10.1214/aos/1176346265

Mathematical Reviews number (MathSciNet)
MR707949

Zentralblatt MATH identifier
0546.62017

JSTOR
links.jstor.org

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

Keywords
Censored data hazard function kernel method Hajek's projection method

Citation

Tanner, Martin A.; Wong, Wing Hung. The Estimation of the Hazard Function from Randomly Censored Data by the Kernel Method. Ann. Statist. 11 (1983), no. 3, 989--993. doi:10.1214/aos/1176346265. https://projecteuclid.org/euclid.aos/1176346265


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