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June, 1984 Asymptotic Equivalence Between the Cox Estimator and the General ML Estimators of Regression and Survival Parameters in the Cox Model
Kent R. Bailey
Ann. Statist. 12(2): 730-736 (June, 1984). DOI: 10.1214/aos/1176346518

Abstract

The usual approach to estimating regression parameters in the Cox regression model uses the partial likelihood. If the covariates are not time-dependent, the model can be stated in terms of the survival function, which allows one to derive a generalized likelihood containing both regression and survival curve parameters. It is shown that, in the absence of ties, an estimator results which is asymptotically equivalent to the partial likelihood estimator. A joint information matrix leads simply to standard errors for both regression and survival curve parameters which are asymptotically correct.

Citation

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Kent R. Bailey. "Asymptotic Equivalence Between the Cox Estimator and the General ML Estimators of Regression and Survival Parameters in the Cox Model." Ann. Statist. 12 (2) 730 - 736, June, 1984. https://doi.org/10.1214/aos/1176346518

Information

Published: June, 1984
First available in Project Euclid: 12 April 2007

zbMATH: 0544.62028
MathSciNet: MR740924
Digital Object Identifier: 10.1214/aos/1176346518

Subjects:
Primary: 62E20
Secondary: 62F12

Rights: Copyright © 1984 Institute of Mathematical Statistics

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Vol.12 • No. 2 • June, 1984
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