Add to ORKGAbstract. This article gives ideas for developing statistics software which can work without user intervention. Some popular methods of bandwidth selection for kernel density estimation (the near-est neighbour, least squares cross-validation, “plug-in ” technique) are discussed. Modifications of the cross-validation criterion are proposed. Two-stage estimators combining these methods with multiplicative bias correction are investigated by simulation means. Key words: kernel density estimation, local bandwidth selection, cross-validation, multiplicative bias correction
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
This note concerns kernel density estimation at a point. It is shown that under a wide variety of ci...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
There has been major progress in recent years in data-based bandwidth selection for kernel density e...
We present a new method for data-based selection of the bandwidth in kernel density estimation which...
Most recently proposed bandwidth selectors in kernel density estimation have been developed with int...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
Recently, much progress has been made on understanding the bandwidth selection problem in kernel den...
Recently, much progress has been made on understanding the bandwidth selection problem in kernel den...
We review the extensive recent literature on automatic, data-based selection of a global smoothing p...
Since the late 1980s, several methods have been considered in the literature to reduce the sample va...
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
A class of data-based bandwidth selection procedures for kernel density estimation is investigated. ...
The choice of bandwidth is crucial to the kernel density estimation KDE. Various bandwidth selection...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
This note concerns kernel density estimation at a point. It is shown that under a wide variety of ci...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
There has been major progress in recent years in data-based bandwidth selection for kernel density e...
We present a new method for data-based selection of the bandwidth in kernel density estimation which...
Most recently proposed bandwidth selectors in kernel density estimation have been developed with int...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
Recently, much progress has been made on understanding the bandwidth selection problem in kernel den...
Recently, much progress has been made on understanding the bandwidth selection problem in kernel den...
We review the extensive recent literature on automatic, data-based selection of a global smoothing p...
Since the late 1980s, several methods have been considered in the literature to reduce the sample va...
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
A class of data-based bandwidth selection procedures for kernel density estimation is investigated. ...
The choice of bandwidth is crucial to the kernel density estimation KDE. Various bandwidth selection...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
This note concerns kernel density estimation at a point. It is shown that under a wide variety of ci...
Kernel estimation of a density based on contaminated data is considered and the important issue of h...