High-dimensional additive hazard models and the Lasso
Statistics Theory
2012-03-06 v2 Statistics Theory
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 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.
Cite
@article{arxiv.1106.4662,
title = {High-dimensional additive hazard models and the Lasso},
author = {Séphane Gaïffas and Agathe Guilloux},
journal= {arXiv preprint arXiv:1106.4662},
year = {2012}
}