The Annals of Statistics

On Using Stratification in the Analysis of Linear Regression Models with Right Censoring

Mendel Fygenson and Mai Zhou

Full-text: Open access

Abstract

We study two modified synthetic data least-squares estimation methods for linear regression models with right censored response variables, unspecified residual distributions and random censoring variables which may not be i.i.d. These methods are the result of an investigation into the use of stratification. We conclude that stratification should be used whether or not the censoring variables are dependent on the covariates. We give the asymptotic results of the estimators and numerical results.

Article information

Source
Ann. Statist., Volume 22, Number 2 (1994), 747-762.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176325494

Digital Object Identifier
doi:10.1214/aos/1176325494

Mathematical Reviews number (MathSciNet)
MR1292539

Zentralblatt MATH identifier
0805.62065

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62J05: Linear regression 62E20: Asymptotic distribution theory 62E25

Keywords
Censored data linear models least-squares estimators stratification

Citation

Fygenson, Mendel; Zhou, Mai. On Using Stratification in the Analysis of Linear Regression Models with Right Censoring. Ann. Statist. 22 (1994), no. 2, 747--762. doi:10.1214/aos/1176325494. https://projecteuclid.org/euclid.aos/1176325494


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