The Annals of Statistics

Regression Analysis with Randomly Right-Censored Data

H. Koul, V. Susarla, and J. Van Ryzin

Full-text: Open access

Abstract

This paper proposes a new estimator of the parameter vector in a linear regression model when the observations are randomly censored on the right and when the error distribution is unknown. This estimator is explicitly defined and easily computable. The paper contains sufficient conditions under which this estimator is mean square consistent and asymptotically normal. A numerical example is given.

Article information

Source
Ann. Statist., Volume 9, Number 6 (1981), 1276-1288.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345644

Digital Object Identifier
doi:10.1214/aos/1176345644

Mathematical Reviews number (MathSciNet)
MR630110

Zentralblatt MATH identifier
0477.62046

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62J05: Linear regression 62P10: Applications to biology and medical sciences 62N05: Reliability and life testing [See also 90B25]

Keywords
Least squares Kaplan-Meier consistent asymptotically normal

Citation

Koul, H.; Susarla, V.; Ryzin, J. Van. Regression Analysis with Randomly Right-Censored Data. Ann. Statist. 9 (1981), no. 6, 1276--1288. doi:10.1214/aos/1176345644. https://projecteuclid.org/euclid.aos/1176345644


Export citation