Linear regression with censoring

C. Srinivasan; M. Zhou

doi:10.1006/jmva.1994.1021

Linear regression with censoring

C. Srinivasan, M. Zhou

Dr. Bing Zhang Department of Statistics

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

Koul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of the ordinary least squares estimator in linear models with censored data. This paper uses counting processes and martingale techniques to provide a proof of the asymptotic normality of the estimator. A detailed analysis of the asymptotic variance is presented.

Original language	English
Pages (from-to)	179-201
Number of pages	23
Journal	Journal of Multivariate Analysis
Volume	49
Issue number	2
DOIs	https://doi.org/10.1006/jmva.1994.1021
State	Published - May 1994

Keywords

Asymptotic distribution
Censored data
Martingale
Regression

ASJC Scopus subject areas

Statistics and Probability
Numerical Analysis
Statistics, Probability and Uncertainty

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10.1006/jmva.1994.1021

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abstract = "Koul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of the ordinary least squares estimator in linear models with censored data. This paper uses counting processes and martingale techniques to provide a proof of the asymptotic normality of the estimator. A detailed analysis of the asymptotic variance is presented.",

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AU - Zhou, M.

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AB - Koul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of the ordinary least squares estimator in linear models with censored data. This paper uses counting processes and martingale techniques to provide a proof of the asymptotic normality of the estimator. A detailed analysis of the asymptotic variance is presented.

KW - Asymptotic distribution

KW - Censored data

KW - Martingale

KW - Regression

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JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

IS - 2

ER -

Linear regression with censoring

Abstract

Keywords

ASJC Scopus subject areas

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