Add to ORKGAbstract: A semiparametric estimate of a density may be formed via the convex combination of a parametric and a nonparametric density estimate. It is shown that the some trimming is often necessary to obtain an appropriate proportion of these estimates. Keywordr: Nonparametric density estimation, bandwidth selection, trimming. 1
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
A semiparametric estimate of a density may be formed via the convex combination of a parametric and ...
Includes bibliographical references (p. 34-35).James L. Powell, Thomas M. Stoker
This paper considers a class of semiparametric estimators that take the form of density-weighted ave...
http://demonstrations.wolfram.com/NonparametricDensityEstimationRobustCrossValidationBandwidth/. Thi...
In contrast to the traditional kernel density estimate which is totally nonparametric, if one has a ...
In contrast to the traditional kernel density estimate which is totally nonparametric, if one has a ...
In this paper, we consider the non-parametric, kernel estimate of the density, f(x), for data drawn ...
A method is proposed for semiparametric estimation where parametric and nonparametric criteria are e...
A new semiparametric method for density deconvolution is proposed, based on a model in which only th...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
There is an intense and partly recent literature focussing on the problems of selecting the bandwidt...
We consider a weighted, nonparametric density estimator for stratified samples. We derive the optima...
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
A semiparametric estimate of a density may be formed via the convex combination of a parametric and ...
Includes bibliographical references (p. 34-35).James L. Powell, Thomas M. Stoker
This paper considers a class of semiparametric estimators that take the form of density-weighted ave...
http://demonstrations.wolfram.com/NonparametricDensityEstimationRobustCrossValidationBandwidth/. Thi...
In contrast to the traditional kernel density estimate which is totally nonparametric, if one has a ...
In contrast to the traditional kernel density estimate which is totally nonparametric, if one has a ...
In this paper, we consider the non-parametric, kernel estimate of the density, f(x), for data drawn ...
A method is proposed for semiparametric estimation where parametric and nonparametric criteria are e...
A new semiparametric method for density deconvolution is proposed, based on a model in which only th...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
There is an intense and partly recent literature focussing on the problems of selecting the bandwidt...
We consider a weighted, nonparametric density estimator for stratified samples. We derive the optima...
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...