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 ℓ1 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.
Citation
Stéphane Gaïffas. Agathe Guilloux. "High-dimensional additive hazards models and the Lasso." Electron. J. Statist. 6 522 - 546, 2012. https://doi.org/10.1214/12-EJS681
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