15 citations · 40 across the 12 of their papers we have counts for
17 papers
On the Dirichlet-kernel Gasser--Müller estimator and its competitors for fixed design regression on the simplex
Hanen Daayeb, Christian Genest, Salah Khardani +2
A Dirichlet-kernel Gasser-Müller (D-GM) estimator is introduced for fixed design regression on the simplex, extending the univariate analog due to Chen [Statist. Sinica, vol. 10(1)…
A new adaptive local polynomial density estimation procedure on complicated domains
Karine Bertin, Nicolas Klutchnikoff, Frédéric Ouimet
This paper presents a novel approach for pointwise estimation of multivariate density functions on known domains of arbitrary dimensions using nonparametric local polynomial estima…
Learning the regularity of multivariate functional data
Omar Kassi, Nicolas Klutchnikoff, Valentin Patilea
Combining information both within and between sample realizations, we propose a simple estimator for the local regularity of surfaces in the functional data framework. The independ…
Adaptive functional principal components analysis
Sunny G. W. Wang, Valentin Patilea, Nicolas Klutchnikoff
Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although s…
Statistical analysis of a hierarchical clustering algorithm with outliers
Nicolas Klutchnikoff, Audrey Poterie, Laurent Rouviere
It is well known that the classical single linkage algorithm usually fails to identify clusters in the presence of outliers. In this paper, we propose a new version of this algorit…
Optimal 1-Wasserstein Distance for WGANs
Arthur Stéphanovitch, Ugo Tanielian, Benoît Cadre +2
The mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues. Motivated by the important question of characterizing the geometrical p…
Minimax properties of Dirichlet kernel density estimators
Karine Bertin, Christian Genest, Nicolas Klutchnikoff +1
This paper considers the asymptotic behavior in -Hölder spaces, and under losses, of a Dirichlet kernel density estimator proposed by Aitchison and Lauder (1985) for the a…
Adaptive estimation of irregular mean and covariance functions
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
Nonparametric estimators for the mean and the covariance functions of functional data are proposed. The setup covers a wide range of practical situations. The random trajectories a…
Clustering multivariate functional data using unsupervised binary trees
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations cou…
Learning the smoothness of noisy curves with application to online curve estimation
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
Combining information both within and across trajectories, we propose a simple estimator for the local regularity of the trajectories of a stochastic process. Independent trajector…
Adaptive estimation of the stationary density of a stochastic differential equation driven by a fractional Brownian motion
Karine Bertin, Nicolas Klutchnikoff, Fabien Panloup +1
We build and study a data-driven procedure for the estimation of the stationary density f of an additive fractional SDE. To this end, we also prove some new concentrations bounds f…
Adaptive regression with Brownian path covariate
Karine Bertin, Nicolas Klutchnikoff
This paper deals with estimation with functional covariates. More precisely, we aim at estimating the regression function of a continuous outcome against a standard Wiener…
Adaptive Density Estimation on Bounded Domains
Karine Bertin, Salima El Kolei, Nicolas Klutchnikoff
We study the estimation, in Lp-norm, of density functions defined on [0,1]^d. We construct a new family of kernel density estimators that do not suffer from the so-called boundary…
Kernel estimation of the intensity of Cox processes
Nicolas Klutchnikoff, Gaspar Massiot
Counting processes often written are used in several applications of biostatistics, notably for the study of chronic diseases. In the case of respirato…
Pointwise Adaptive Estimation of the MarginalDensity of a Weakly Dependent Process
Karine Bertin, Nicolas Klutchnikoff
This paper is devoted to the estimation of the common marginal density function of weakly dependent processes. The accuracy of estimation is measured using pointwise risks. We prop…
On clustering procedures and nonparametric mixture estimation
Stéphane Auray, Nicolas Klutchnikoff, Laurent Rouvière
This paper deals with nonparametric estimation of conditional den-sities in mixture models in the case when additional covariates are available. The proposed approach consists of p…
Minimax properties of beta kernel density estimators
Karine Bertin, Nicolas Klutchnikoff
In this paper, we are interested in the study of beta kernel estimators from an asymptotic minimax point of view. It is well known that beta kernel estimators are, on the contrary…