Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates

TL;DR

This paper introduces an adaptive method for estimating the baseline hazard function in high-dimensional Cox models, combining Lasso-based regression with model selection to improve estimation accuracy.

Contribution

It proposes a novel two-step procedure that integrates Lasso estimation and model selection for the baseline function in high-dimensional settings.

Findings

Establishes a non-asymptotic oracle inequality for the estimator.

Highlights the impact of covariate dimension on convergence rates.

Provides a practical approach for high-dimensional survival analysis.

Abstract

The purpose of this article is to provide an adaptive estimator of the baseline function in the Cox model with high-dimensional covariates. We consider a two-step procedure : first, we estimate the regression parameter of the Cox model via a Lasso procedure based on the partial log-likelihood, secondly, we plug this Lasso estimator into a least-squares type criterion and then perform a model selection procedure to obtain an adaptive penalized contrast estimator of the baseline function. Using non-asymptotic estimation results stated for the Lasso estimator of the regression parameter, we establish a non-asymptotic oracle inequality for this penalized contrast estimator of the baseline function, which highlights the discrepancy of the rate of convergence when the dimension of the covariates increases.