Adaptive kernel estimation of the baseline function in the Cox model, with high-dimensional covariates
Abstract
The aim of this article is to propose a novel kernel estimator of the baseline function in a general high-dimensional Cox model, for which we derive non-asymptotic rates of convergence. To construct our estimator, we first estimate the regression parameter in the Cox model via a Lasso procedure. We then plug this estimator into the classical kernel estimator of the baseline function, obtained by smoothing the so-called Breslow estimator of the cumulative baseline function. We propose and study an adaptive procedure for selecting the bandwidth, in the spirit of Gold-enshluger and Lepski (2011). We state non-asymptotic oracle inequalities for the final estimator, which reveal the reduction of the rates of convergence when the dimension of the covariates grows.
Cite
@article{arxiv.1507.01397,
title = {Adaptive kernel estimation of the baseline function in the Cox model, with high-dimensional covariates},
author = {Agathe Guilloux and Sarah Lemler and Marie-Luce Taupin},
journal= {arXiv preprint arXiv:1507.01397},
year = {2015}
}