activity
20052026
most citedNonparametric Estimation of the Regression Function in an Errors-in-Variables Model

12 citations · 28 across the 15 of their papers we have counts for

collaborators

18 papers

math.ST2026

Faster Learning under Relaxed Local Differential Privacy

Cristina Butucea, Huiyun Tang, Marie-Luce Taupin

We consider density estimation under the relaxed local differential privacy condition that the privatized distributions are -close in total variation distance. We show that addi…

math.ST2025

A model-free Screening procedure

J Dedecker, M L Taupin, A S Tocquet

In this article, we propose a generic screening method for selecting explanatory variables correlated with the response variable Y . We make no assumptions about the existence of a…

math.ST2024

Asymptotic confidence interval for R2 in multiple linear regression

J Dedecker, Odelia Guedj, Marie-Luce Taupin

Following White's approach of robust multiple linear regression, we give asymptotic confidence intervals for the multiple correlation coefficient R2 under minimal moment conditions…

math.ST2020

Risk upper bounds for RKHS ridge group sparse estimator in the regression model with non-Gaussian and non-bounded error

Halaleh Kamari, Sylvie Huet, Marie-Luce Taupin

We consider the problem of estimating a meta-model of an unknown regression model with non-Gaussian and non-bounded error. The meta-model belongs to a reproducing kernel Hilbert sp…

stat.ML2019

RKHSMetaMod: An R package to estimate the Hoeffding decomposition of a complex model by solving RKHS ridge group sparse optimization problem

Halaleh Kamari, Sylvie Huet, Marie-Luce Taupin

In this paper, we propose an R package, called RKHSMetaMod, that implements a procedure for estimating a meta-model of a complex model. The meta-model approximates the Hoeffding de…

stat.AP2017

Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale

Jordi Ferrer Savall, Damien Franqueville, Pierre Barbillon +5

Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate…