Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates - Sorbonne Université Accéder directement au contenu
Article Dans Une Revue Journal of Multivariate Analysis Année : 2016

Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates

Résumé

We 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 Goldenshluger and Lepski (2011). We state non-asymptotic oracle inequalities for the final estimator, which leads to a reduction in the rate of convergence when the dimension of the covariates grows.

Dates et versions

hal-01327412 , version 1 (06-06-2016)

Identifiants

Citer

Agathe Guilloux, Sarah Lemler, Marie-Luce Taupin. Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates. Journal of Multivariate Analysis, 2016, 148, pp.141-159. ⟨10.1016/j.jmva.2016.03.002⟩. ⟨hal-01327412⟩
357 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More