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

Socpus ID

0242550935 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0242550935

This document is currently not available here.

Plum Print visual indicator of research metrics
PlumX Metrics
  • Citations
    • Citation Indexes: 5
  • Usage
    • Abstract Views: 4
  • Captures
    • Readers: 1
see details

Share

COinS