Copyright © 2012 Ali Al-Kenani and Keming Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The article is available through the Brunel Open Access Publishing Fund.We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically optimal bandwidth c...
Quantile and semiparametric M estimation are methods for estimating a censored linear regression mod...
A bandwidth selection method is proposed for kernel density estimation. This is based on the straigh...
Bandwidth selection in kernel density estimation is one of the fundamental model selection problems ...
In this article, we summarize some quantile estimators and related bandwidth selection methods and g...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
AbstractThis note concentrates on the nonparametric estimation of a probability mass function (p.m.f...
Least squares cross-validation (CV) methods are often used for automated bandwidth selection. We sho...
AbstractThis paper studies the risks and bandwidth choices of a kernel estimate of the underlying de...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
For the data based choice of the bandwidth of a kernel density estimator, several methods have recen...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
Includes bibliographical references (p. 34-35).James L. Powell, Thomas M. Stoker
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
We propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is...
The two most popular bandwidth choice rules for kernel HAC estimation have been proposed by Andrews ...
Quantile and semiparametric M estimation are methods for estimating a censored linear regression mod...
A bandwidth selection method is proposed for kernel density estimation. This is based on the straigh...
Bandwidth selection in kernel density estimation is one of the fundamental model selection problems ...
In this article, we summarize some quantile estimators and related bandwidth selection methods and g...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
AbstractThis note concentrates on the nonparametric estimation of a probability mass function (p.m.f...
Least squares cross-validation (CV) methods are often used for automated bandwidth selection. We sho...
AbstractThis paper studies the risks and bandwidth choices of a kernel estimate of the underlying de...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
For the data based choice of the bandwidth of a kernel density estimator, several methods have recen...
Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative da...
Includes bibliographical references (p. 34-35).James L. Powell, Thomas M. Stoker
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
We propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is...
The two most popular bandwidth choice rules for kernel HAC estimation have been proposed by Andrews ...
Quantile and semiparametric M estimation are methods for estimating a censored linear regression mod...
A bandwidth selection method is proposed for kernel density estimation. This is based on the straigh...
Bandwidth selection in kernel density estimation is one of the fundamental model selection problems ...