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

    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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