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

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01120683
Contributor : Sarah Lemler <>
Submitted on : Saturday, February 28, 2015 - 1:03:16 PM
Last modification on : Tuesday, December 11, 2018 - 4:22:03 PM
Long-term archiving on : Friday, May 29, 2015 - 10:06:21 AM

Files

GuillouxLemlerTaupin_ModelSele...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01120683, version 1
  • ARXIV : 1503.00226

Citation

Agathe Guilloux, Sarah Lemler, Marie-Luce Taupin. Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates. 2015. ⟨hal-01120683v1⟩

Share

Metrics

Record views

145

Files downloads

49