Title

Kernel contrasts: A data-based method of choosing smoothing parameters in nonparametric density estimation

Authors

Authors

I. A. Ahmad;I. S. Ran

Comments

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Abbreviated Journal Title

J. Nonparametr. Stat.

Keywords

kernel smoothing; kernel contrasts; density estimation; cross; validation; optimal bandwidth; Monte Carlo method; asymptotic normality; integrated mean square error; INTEGRATED SQUARE ERROR; BANDWIDTH SELECTION; CROSS-VALIDATION; CHOICE; Statistics & Probability

Abstract

By introducing the concept of 'kernel contrasts' as an error criterion, with a global norm, which is taken here to be the L-2 norm that is usually used in nonpararnetric density estimation, it is possible to provide a completely data-based choice of the bandwidth, which is asymptotically equivalent to the optimal theoretical choice. The density estimate based on this data-based choice of the bandwidth has desirable properties. Monte Carlo studies and Studies of real data sets show how much better this new method is over usual other methods Such as unbiased cross-validation method. The technique is also extendible in a direct fashion to multivariate setting.

Journal Title

Journal of Nonparametric Statistics

Volume

16

Issue/Number

5

Publication Date

1-1-2004

Document Type

Article

Language

English

First Page

671

Last Page

707

WOS Identifier

WOS:000222385000001

ISSN

1048-5252

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