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.
Type de document :
Pré-publication, Document de travail
2015
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https://hal.archives-ouvertes.fr/hal-01120683
Contributeur : Sarah Lemler <>
Soumis le : dimanche 1 mars 2015 - 22:06:51
Dernière modification le : mardi 11 décembre 2018 - 16:22:03
Document(s) archivé(s) le : mardi 2 juin 2015 - 09:31:03

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  • HAL Id : hal-01120683, version 2
  • ARXIV : 1503.00226

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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-01120683v2〉

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