Local Bandwidth Selection for Density Estimation

1992 ◽  
pp. 111-116 ◽  
Author(s):  
Lori A. Thombs ◽  
Simon J. Sheather
Keyword(s):  
Density Estimation ◽  
Bandwidth Selection ◽  
Selection For

scholarly journals Bandwidth selection for kernel density estimation using Fourier domain constraints

2016 ◽  
Vol 10 (3) ◽  
pp. 280-283
Author(s):  
Alexander Suhre ◽  
Orhan Arikan ◽  
Ahmed Enis Cetin
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Bandwidth Selection ◽  
Kernel Density ◽  
Fourier Domain ◽  
Selection For ◽  
Domain Constraints

Optimal bandwidth selection for multivariate kernel deconvolution density estimation

Test ◽  
2007 ◽  
Vol 17 (1) ◽  
pp. 138-162 ◽  
Author(s):  
Élie Youndjé ◽  
Martin T. Wells
Keyword(s):  
Density Estimation ◽  
Bandwidth Selection ◽  
Optimal Bandwidth ◽  
Selection For

scholarly journals Bandwidth selection for kernel density estimation: a review of fully automatic selectors

2013 ◽  
Vol 97 (4) ◽  
pp. 403-433 ◽  
Author(s):  
Nils-Bastian Heidenreich ◽  
Anja Schindler ◽  
Stefan Sperlich
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Bandwidth Selection ◽  
Kernel Density ◽  
Fully Automatic ◽  
Selection For

Bandwidth selection for kernel log-density estimation

2016 ◽  
Vol 103 ◽  
pp. 56-67 ◽  
Author(s):  
Martin L. Hazelton ◽  
Murray P. Cox
Keyword(s):  
Density Estimation ◽  
Bandwidth Selection ◽  
Selection For

scholarly journals Optimal Bandwidth Selection for Kernel Density Functionals Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Su Chen
Keyword(s):  
Density Estimation ◽  
Bandwidth Selection ◽  
Kernel Density ◽  
Least Square ◽  
Density Functionals ◽  
Selection Methods ◽  
Optimal Bandwidth ◽  
Location Parameters ◽  
The Mean ◽  
Selection For

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)dxwith appropriate choice ofγ(x)and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations.∫γ(x)f2(x)dxcan be estimated nonparametrically via kernel density functionals estimation (KDFE). However, the optimal bandwidth selection for KDFE of∫γ(x)f2(x)dxhas 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)dxare 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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