6 citations · 27 across the 19 of their papers we have counts for
6 papers · 1 filter
Learning the intensity of time events with change-points
Mokhtar Zahdi Alaya, Stéphane Gaïffas, Agathe Guilloux
We consider the problem of learning the inhomogeneous intensity of a counting process, under a sparse segmentation assumption. We introduce a weighted total-variation penalization,…
Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates
Agathe Guilloux, Sarah Lemler, Marie-Luce Taupin
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…
A penalized algorithm for event-specific rate models for recurrent events
Olivier Bouaziz, Agathe Guilloux
We introduce a covariate-specific total variation penalty in two semiparametric models for the rate function of recurrent event process. The two models are a stratified Cox model,…
High-dimensional additive hazard models and the Lasso
Séphane Gaïffas, Agathe Guilloux
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 wit…
Learning and adaptive estimation for marker-dependent counting processes
Stéphane Gaïffas, Agathe Guilloux
We consider the problem of statistical learning for the intensity of a counting process with covariates. In this context, we introduce an empirical risk, and prove risk bounds for…
Adaptive estimation of the conditional intensity of marker-dependent counting processes
F. Comte, S. Gaïffas, A. Guilloux
We propose in this work an original estimator of the conditional intensity of a marker-dependent counting process, that is, a counting process with covariates. We use model selecti…