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On optimal data-based bandwidth selection in kernel density estimation

On optimal data-based bandwidth selection in kernel density estimation Abstract A bandwidth selection method is proposed for kernel density estimation. This is based on the straightforward idea of plugging estimates into the usual asymptotic representation for the optimal bandwidth, but with two important modifications. The result is a bandwidth selector with the, by nonparametric standards, extremely fast asymptotic rate of convergence of n−½ where n ↑ ∞ denotes sample size. Comparison is given to other bandwidth selection methods, and small sample impact is investigated. This content is only available as a PDF. © 1991 Biometrika Trust http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrika Oxford University Press

On optimal data-based bandwidth selection in kernel density estimation

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References (8)

Publisher
Oxford University Press
Copyright
© 1991 Biometrika Trust
ISSN
0006-3444
eISSN
1464-3510
DOI
10.1093/biomet/78.2.263
Publisher site
See Article on Publisher Site

Abstract

Abstract A bandwidth selection method is proposed for kernel density estimation. This is based on the straightforward idea of plugging estimates into the usual asymptotic representation for the optimal bandwidth, but with two important modifications. The result is a bandwidth selector with the, by nonparametric standards, extremely fast asymptotic rate of convergence of n−½ where n ↑ ∞ denotes sample size. Comparison is given to other bandwidth selection methods, and small sample impact is investigated. This content is only available as a PDF. © 1991 Biometrika Trust

Journal

BiometrikaOxford University Press

Published: Jun 1, 1991

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