Title

Analysis of kernel density estimation of functions of random variables

Authors

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

DOI Link

http://dx.doi.org/10.1080/10485250310001605441

Language

English

First Page

579

Last Page

605

WOS Identifier

WOS:000186756300014

ISSN

1048-5252

Recommended Citation

"Analysis of kernel density estimation of functions of random variables" (2003). Faculty Bibliography 2000s. 3586.
https://stars.library.ucf.edu/facultybib2000/3586