16 citations · 46 across the 5 of their papers we have counts for
7 papers
Nonparametric estimation in a regression model with additive and multiplicative noise
Christophe Chesneau, Salima El Kolei, Junke Kou +1
In this paper, we consider an unknown functional estimation problem in a general nonparametric regression model with the feature of having both multiplicative and additive noise.We…
On adaptive wavelet estimation of a class of weighted densities
Fabien Navarro, Christophe Chesneau, Jalal Fadili
We investigate the estimation of a weighted density taking the form , where denotes an unknown density, the associated distribution function and is a known (no…
Block thresholding for wavelet-based estimation of function derivatives from a heteroscedastic multichannel convolution model
Fabien Navarro, Christophe Chesneau, Jalal Fadili +1
We observe heteroscedastic stochastic processes , where for any and , is the convolution product of an unknown functi…
Adaptive estimation of an additive regression function from weakly dependent data
Christophe Chesneau, Jalal M. Fadili, Bertrand Maillot
A -dimensional nonparametric additive regression model with dependent observations is considered. Using the marginal integration technique and wavelets methodology, we develop a…
The degrees of freedom of the Lasso for general design matrix
Charles Dossal, Maher Kachour, Jalal M. Fadili +2
In this paper, we investigate the degrees of freedom ($\dof$) of penalized minimization (also known as the Lasso) for linear regression models. We give a closed-form expre…
Wavelet block thresholding for samples with random design: a minimax approach under the risk
Christophe Chesneau
We consider the regression model with (known) random design. We investigate the minimax performances of an adaptive wavelet block thresholding estimator under the ri…
Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators
Christophe Chesneau, Guillaume Lecué
We study the performances of an adaptive procedure based on a convex combination, with data-driven weights, of term-by-term thresholded wavelet estimators. For the bounded regressi…