Title
Analysis Of Kernel Density Estimation Of Functions Of Random Variables
Keywords
Asymptotic expansion; Bandwidth selection; Central limit theorem; Density estimation; Functions of random variables; Kernel contrast
Abstract
In the current investigation, the problem of estimating the probability density of a function of m independent identically distributed random variables, g(X1..... Xm) 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.
Publication Date
8-1-2003
Publication Title
Journal of Nonparametric Statistics
Volume
15
Issue
4-5
Number of Pages
579-605
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1080/10485250310001605441
Copyright Status
Unknown
Socpus ID
0242550935 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0242550935
STARS Citation
Ahmad, Ibrahim A. and Mugdadi, A. R., "Analysis Of Kernel Density Estimation Of Functions Of Random Variables" (2003). Scopus Export 2000s. 1649.
https://stars.library.ucf.edu/scopus2000/1649
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