5 citations · 9 across the 9 of their papers we have counts for
12 papers
Estimation and variable selection in high dimension in nonlinear mixed-effects models
Antoine Caillebotte, Estelle Kuhn, Sarah Lemler
We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates among…
Nonparametric estimation of the stationary density for Hawkes-diffusion systems with known and unknown intensity
Chiara Amorino, Charlotte Dion-Blanc, Arnaud Gloter +1
We investigate the nonparametric estimation problem of the density , representing the stationary distribution of a two-dimensional system $\left(Z_t\right)_{t \in[0, T]}=\left(X…
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Charlotte Baey, Maud Delattre, Estelle Kuhn +2
Latent variable models are powerful tools for modeling complex phenomena involving in particular partially observed data, unobserved variables or underlying complex unknown structu…
Estimation and variable selection in high dimension in a causal joint model of survival times and longitudinal outcomes with random effects
Antoine Caillebotte, Estelle Kuhn, Sarah Lemler
We consider a joint survival and mixed-effects model to explain the survival time from longitudinal data and high-dimensional covariates in a population. The longitudinal data is m…
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…
On the nonparametric inference of coefficients of self-exciting jump-diffusion
Chiara Amorino, Charlotte Dion, Arnaud Gloter +1
In this paper, we consider a one-dimensional diffusion process with jumps driven by a Hawkes process. We are interested in the estimations of the volatility function and of the jum…
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…
Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model
Sarah Lemler
In a general counting process setting, we consider the problem of obtaining a prognostic on the survival time adjusted on covariates in high-dimension. Towards this end, we constru…