AbstractThis paper deals with non-parametric density estimation for spatial data. We study the asymptotic properties of a new recursive version of the Parzen–Rozenblatt estimator. The mean square error and an almost sure convergence result with rate of such estimator are derived
International audienceHall and Marron (1987) introduced kernel estimators of integrals of various sq...
AbstractWe construct a simple algorithm, based on Newton's method, which permits asymptotic minimiza...
We construct a simple algorithm, based on Newton's method, which permits asymptotic minimization of ...
International audienceThis paper deals with non-parametric density estimation for spatial data. We s...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
We propose a nonparametric method for density estimation over (possibly complicated) spatial domains...
In this thesis, we are interested in recursive methods that allow to update sequentially estimates i...
Many directional data such as wind directions can be collected extremely easily so that experiments ...
International audienceIn this paper, we propose a nonparametric method to estimate the spatial densi...
International audienceA non-parametric level set estimator of the density of a stationary d-dimensi...
We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorizat...
A novel non-parametric density estimator is developed based on geometric principles. A penalised cen...
International audienceHall and Marron (1987) introduced kernel estimators of integrals of various sq...
AbstractWe construct a simple algorithm, based on Newton's method, which permits asymptotic minimiza...
We construct a simple algorithm, based on Newton's method, which permits asymptotic minimization of ...
International audienceThis paper deals with non-parametric density estimation for spatial data. We s...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
We propose a nonparametric method for density estimation over (possibly complicated) spatial domains...
In this thesis, we are interested in recursive methods that allow to update sequentially estimates i...
Many directional data such as wind directions can be collected extremely easily so that experiments ...
International audienceIn this paper, we propose a nonparametric method to estimate the spatial densi...
International audienceA non-parametric level set estimator of the density of a stationary d-dimensi...
We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorizat...
A novel non-parametric density estimator is developed based on geometric principles. A penalised cen...
International audienceHall and Marron (1987) introduced kernel estimators of integrals of various sq...
AbstractWe construct a simple algorithm, based on Newton's method, which permits asymptotic minimiza...
We construct a simple algorithm, based on Newton's method, which permits asymptotic minimization of ...