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A Brief Survey of Bandwidth Selection for Density Estimation

A Brief Survey of Bandwidth Selection for Density Estimation Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

A Brief Survey of Bandwidth Selection for Density Estimation

A Brief Survey of Bandwidth Selection for Density Estimation

Journal of the American Statistical Association , Volume 91 (433): 7 – Mar 1, 1996

Abstract

Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts.

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References (51)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1080/01621459.1996.10476701
Publisher site
See Article on Publisher Site

Abstract

Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts.

Journal

Journal of the American Statistical AssociationTaylor & Francis

Published: Mar 1, 1996

Keywords: Bandwidth selection; Kernel density estimation; Nonparametric curve estimation; Smoothing parameter selection

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