Analysis of kernel density estimation of functions of random variables

    Authors

    I. A. Ahmad;A. R. Mugdadi

    Comments

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    Abbreviated Journal Title

    J. Nonparametr. Stat.

    Keywords

    functions of random variables; density estimation; central limit; theorem; asymptotic expansion; kernel contrast; bandwidth selection; CONDITIONAL U-STATISTICS; Statistics & Probability

    Abstract

    In the current investigation, the problem,of estimating the probability density of a function of in in dependent identically distributed random variables, g(X-1, ..., X-m) is considered. Defining the integrated square contrast (ISC)and its mean (MISC), we study the central limit theorem of (ISO-MISC) as well as the second order approximation of both ISC and MISC. Via simulation and also using real data, we address some of the practical aspects of choosing the optimal bandwidth which minimizes the asymptotic MISC and its data based analog which minimizes ISC.

    Journal Title

    Journal of Nonparametric Statistics

    Volume

    15

    Issue/Number

    4-5

    Publication Date

    1-1-2003

    Document Type

    Article

    Language

    English

    First Page

    579

    Last Page

    605

    WOS Identifier

    WOS:000186756300014

    ISSN

    1048-5252

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