7 papers
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…
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…