This paper introduces new adaptive versions of the variable location density estimator which, for the first time, achieve bias improvement by an order of magnitude at the boundaries, as well as affording the usual higher order bias in the interior of th
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
The nature of the kernel density estimator (KDE) is to find the underlying probability density funct...
Kernel density estimation is a widely used method for estimating a distribution based on a sample of...
This paper introduces new adaptive versions of the variable location density estimator which, for th...
[[abstract]]Variable (bandwidth) kernel density estimation (Abramson (1982, Ann. Statist., 10, 1217-...
Variable (bandwidth) kernel density estimation (Abramson (1982,Ann. Statist.,10, 1217–1223)) and a k...
We propose and study a kernel estimator of a density in which the kernel is adapted to the data but ...
This article proposes a new data-driven method for selecting the smoothing parameter involved in the...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Summary. Methods for improving the basic kernel density estimator in-clude variable locations, varia...
A location-adaptive hybrid of the fixed-bandwidth kernel density estimate and the nearest-neighbor d...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
AbstractA location-adaptive hybrid of the fixed-bandwidth kernel density estimate and the nearest-ne...
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
The nature of the kernel density estimator (KDE) is to find the underlying probability density funct...
Kernel density estimation is a widely used method for estimating a distribution based on a sample of...
This paper introduces new adaptive versions of the variable location density estimator which, for th...
[[abstract]]Variable (bandwidth) kernel density estimation (Abramson (1982, Ann. Statist., 10, 1217-...
Variable (bandwidth) kernel density estimation (Abramson (1982,Ann. Statist.,10, 1217–1223)) and a k...
We propose and study a kernel estimator of a density in which the kernel is adapted to the data but ...
This article proposes a new data-driven method for selecting the smoothing parameter involved in the...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Summary. Methods for improving the basic kernel density estimator in-clude variable locations, varia...
A location-adaptive hybrid of the fixed-bandwidth kernel density estimate and the nearest-neighbor d...
AbstractIn some applications of kernel density estimation the data may have a highly non-uniform dis...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
AbstractA location-adaptive hybrid of the fixed-bandwidth kernel density estimate and the nearest-ne...
© 2017 Springer Science+Business Media, LLC Kernel smoothing of spatial point data can often be impr...
The nature of the kernel density estimator (KDE) is to find the underlying probability density funct...
Kernel density estimation is a widely used method for estimating a distribution based on a sample of...
DOI
Add to ORKG