High-dimensional additive hazards models and the Lasso

By S. Gaiffas, Agathe Guilloux. 2011

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Abstract: We consider a general high-dimensional additive hazards model in a non-asymptotic setting, including regression for censored-data. In this context, we consider a Lasso estimator with a fully data-driven `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.

Subfields: Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6481099d2ff390b3e4776325e3a7b1d9ee83603e