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October 1999 Efficient estimation of the partly linear additive Cox model
Jian Huang
Ann. Statist. 27(5): 1536-1563 (October 1999). DOI: 10.1214/aos/1017939141

Abstract

The partly linear additive Cox model is an extension of the (linear) Cox model and allows flexible modeling of covariate effects semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this model with right-censored data using polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) required for estimation of the nonparametric components, the estimator of the finite-dimensional regression parameter is root-$n$ consistent, asymptotically normal and achieves the semiparametric information bound. Rates of convergence for the estimators of the nonparametric components are obtained. They are comparable to the rates in nonparametric regression. Implementation of the estimation approach can be done easily and is illustrated by using a simulated example.

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Jian Huang. "Efficient estimation of the partly linear additive Cox model." Ann. Statist. 27 (5) 1536 - 1563, October 1999. https://doi.org/10.1214/aos/1017939141

Information

Published: October 1999
First available in Project Euclid: 23 September 2004

zbMATH: 0977.62035
MathSciNet: MR2000M:62015
Digital Object Identifier: 10.1214/aos/1017939141

Subjects:
Primary: 62G05, 62G20
Secondary: 62G07, 62P99

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.27 • No. 5 • October 1999
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