The Annals of Statistics

Asymptotic Normality of the `Synthetic Data' Regression Estimator for Censored Survival Data

Mai Zhou

Full-text: Open access

Abstract

This article studies the large sample behavior of the censored data least squares estimator derived from the synthetic data method proposed by Leurgans and Zheng. The asymptotic distributions are derived by representing the estimator as a martingale plus a higher-order remainder term. Recently developed counting process techniques are used. The results are then compared to the censored regression estimator of Koul, Susarla and Van Ryzin.

Article information

Source
Ann. Statist., Volume 20, Number 2 (1992), 1002-1021.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348667

Digital Object Identifier
doi:10.1214/aos/1176348667

Mathematical Reviews number (MathSciNet)
MR1165603

Zentralblatt MATH identifier
0748.62024

JSTOR
links.jstor.org

Subjects
Primary: 62G10: Hypothesis testing
Secondary: 62P10: Applications to biology and medical sciences 62N05: Reliability and life testing [See also 90B25]

Keywords
Censored data linear regression asymptotic distribution

Citation

Zhou, Mai. Asymptotic Normality of the `Synthetic Data' Regression Estimator for Censored Survival Data. Ann. Statist. 20 (1992), no. 2, 1002--1021. doi:10.1214/aos/1176348667. https://projecteuclid.org/euclid.aos/1176348667


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