Add to ORKGWe propose a new estimator for boundary correction for kernel density estimation. Our method is based on local Bayes techniques of Hjort (Bayesian Statist. 5 (1996) 223). The resulting estimator is semiparametric type estimator: a weighted average of an initial guess and the ordinary reflection method estimator. The proposed estimator is seen to perform quite well compared to other existing well-known estimators for densities which have the shoulder condition at the endpoints.Kernel density estimation Boundary effects Linear Bayes methods Mean squared error
Kernel density estimation is a technique for estimation of probability density function that is a mu...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
The revised version of the paper Kernel Density Estimation with Ripley's Circumferential Correction ...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
We propose a new method of boundary correction for kernel density estimation. The technique is a kin...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
If a probability density function has bounded support, kernel density estimates often overspill the ...
Without correction, kernel density estimates suffer from boundary effects. Many boundary corrections...
Kernel estimators of both density and regression functions are not consistent near the nite end poin...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
There are various methods for estimating a density. A group of methods which estimate the density as...
We consider kernel-type methods for estimation of a density on [0, 1] which eschew explicit boundary...
In this thesis, we propose a new estimator for improve boundary effects in kernel estimator of the ...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
The revised version of the paper Kernel Density Estimation with Ripley's Circumferential Correction ...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
We propose a new method of boundary correction for kernel density estimation. The technique is a kin...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
If a probability density function has bounded support, kernel density estimates often overspill the ...
Without correction, kernel density estimates suffer from boundary effects. Many boundary corrections...
Kernel estimators of both density and regression functions are not consistent near the nite end poin...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
There are various methods for estimating a density. A group of methods which estimate the density as...
We consider kernel-type methods for estimation of a density on [0, 1] which eschew explicit boundary...
In this thesis, we propose a new estimator for improve boundary effects in kernel estimator of the ...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
The revised version of the paper Kernel Density Estimation with Ripley's Circumferential Correction ...