Title

Kernel Contrasts: A Data-Based Method Of Choosing Smoothing Parameters In Nonparametric Density Estimation

Keywords

Asymptotic normality; Cross-validation; Density estimation; Integrated mean square error; Kernel contrasts; Kernel smoothing; Monte Carlo method; Optimal bandwidth

Abstract

By introducing the concept of 'kernel contrasts' as an error criterion, with a global norm, which is taken here to be the L2 norm that is usually used in nonparametric 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.

Publication Date

10-1-2004

Publication Title

Journal of Nonparametric Statistics

Volume

16

Issue

5

Number of Pages

671-707

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/10485250310001652610

Socpus ID

3042693662 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/3042693662

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