High-dimensional additive hazard models and the Lasso

Séphane Gaïffas; Agathe Guilloux

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arXiv · 1106.4662

High-dimensional additive hazard models and the Lasso

Séphane Gaïffas·Agathe Guilloux

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Abstract

We consider a general high-dimensional additive hazard model in a non-asymptotic setting, including regression for censored-data. In this context, we consider a Lasso estimator with a fully data-driven $\ell_1$ penalization, which is tuned for the estimation problem at hand. We prove sharp oracle inequalities for this estimator. Our analysis involves a new "data-driven" Bernstein's inequality, that is of independent interest, where the predictable variation is replaced by the optional variation.

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Séphane Gaïffas, Agathe Guilloux. 2012-03-02. High-dimensional additive hazard models and the Lasso. https://arxiv.org/abs/1106.4662

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