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.
Publié le : 1999-10-14
Classification:
Additive regression,
asymptotic normality,
right-censored data,
partial likelihood,
polynomial splines,
projection,
rate of convergence,
semiparametric information bound,
62G05,
62G20,
62G07,
62P99
@article{1017939141,
author = {Huang, Jian},
title = {Efficient estimation of the partly linear additive Cox
model},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 1536-1563},
language = {en},
url = {http://dml.mathdoc.fr/item/1017939141}
}
Huang, Jian. Efficient estimation of the partly linear additive Cox
model. Ann. Statist., Tome 27 (1999) no. 4, pp. 1536-1563. http://gdmltest.u-ga.fr/item/1017939141/