Optimal Bandwidth Selection for Kernel Density Functionals Estimation

The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to densit...

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Main Author: Su Chen
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2015/242683
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author Su Chen
author_facet Su Chen
author_sort Su Chen
collection DOAJ
description The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(x)f2(x)dx with appropriate choice of γ(x) and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(x)f2(x)dx can be estimated nonparametrically via kernel density functionals estimation (KDFE). However, the optimal bandwidth selection for KDFE of ∫γ(x)f2(x)dx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE) of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(x)f2(x)dx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb”) and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.
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spelling doaj-art-b3ff46efb63a4b29ad50bac82b668b7c2025-08-20T03:20:51ZengWileyJournal of Probability and Statistics1687-952X1687-95382015-01-01201510.1155/2015/242683242683Optimal Bandwidth Selection for Kernel Density Functionals EstimationSu Chen0Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152, USAThe choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(x)f2(x)dx with appropriate choice of γ(x) and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(x)f2(x)dx can be estimated nonparametrically via kernel density functionals estimation (KDFE). However, the optimal bandwidth selection for KDFE of ∫γ(x)f2(x)dx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE) of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(x)f2(x)dx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb”) and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.http://dx.doi.org/10.1155/2015/242683
spellingShingle Su Chen
Optimal Bandwidth Selection for Kernel Density Functionals Estimation
Journal of Probability and Statistics
title Optimal Bandwidth Selection for Kernel Density Functionals Estimation
title_full Optimal Bandwidth Selection for Kernel Density Functionals Estimation
title_fullStr Optimal Bandwidth Selection for Kernel Density Functionals Estimation
title_full_unstemmed Optimal Bandwidth Selection for Kernel Density Functionals Estimation
title_short Optimal Bandwidth Selection for Kernel Density Functionals Estimation
title_sort optimal bandwidth selection for kernel density functionals estimation
url http://dx.doi.org/10.1155/2015/242683
work_keys_str_mv AT suchen optimalbandwidthselectionforkerneldensityfunctionalsestimation