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Filtered kernel density estimation
Author(s) -
Marchette David
Publication year - 2009
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.17
Subject(s) - multivariate kernel density estimation , kernel density estimation , estimator , variable kernel density estimation , density estimation , mathematics , nonparametric statistics , probability density function , bandwidth (computing) , kernel (algebra) , statistics , filter (signal processing) , computer science , algorithm , kernel method , artificial intelligence , discrete mathematics , support vector machine , telecommunications , computer vision
This article describes a multiple‐bandwidth version of the kernel estimator for nonparametric probability density estimation, in which the bandwidths are chosen using a set of functions, called filter functions, which determine the support of the density appropriate to the different bandwidths. These filter functions are usually defined using a normal mixture fit to the data. Thus the estimator uses different bandwidths in different regions of the support of the distribution, as controlled by the filter functions. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Density Estimation