5 citations · 5 across the 3 of their papers we have counts for
6 papers
Neuronal Network Inference and Membrane Potential Model using Multivariate Hawkes Processes
Anna Bonnet, Charlotte Dion, François Gindraud +1
In this work, we propose to catch the complexity of the membrane potential's dynamic of a motoneuron between its spikes, taking into account the spikes from other neurons around. O…
Neural networks to predict survival from RNA-seq data in oncology
Mathilde Sautreuil, Sarah Lemler, Paul-Henry Cournède
Survival analysis consists of studying the elapsed time until an event of interest, such as the death or recovery of a patient in medical studies. This work explores the potential…
Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process
Charlotte Dion, Sarah Lemler
We consider a 1-dimensional diffusion process X with jumps. The particularity of this model relies in the jumps which are driven by a multidimensional Hawkes process denoted N. Thi…
Exponential ergodicity for diffusions with jumps driven by a Hawkes process
Charlotte Dion, Sarah Lemler, Eva Löcherbach
In this paper, we introduce a new class of processes which are diffusions with jumps driven by a multivariate nonlinear Hawkes process. Our goal is to study their long-time behavio…
Adaptive kernel estimation of the baseline function in the Cox model, with high-dimensional covariates
Agathe Guilloux, Sarah Lemler, Marie-Luce Taupin
The aim of this article is to propose a novel kernel estimator of the baseline function in a general high-dimensional Cox model, for which we derive non-asymptotic rates of converg…
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…