SMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION

Date Issued:: 2004
Abstract/Description:: This study intends to build up new techniques for how to obtain completely data-driven choices of the smoothing parameter in functional estimation, within the confines of minimal assumptions. The focus of the study will be within the framework of the estimation of the distribution function, the density function and their multivariable extensions along with some of their functionals such as the location and the integrated squared derivatives.

Title:	SMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION.	28 views 12 downloads
Name(s):	Amezziane, Mohamed, Author Ahmad, Ibrahim, Committee Chair University of Central Florida, Degree Grantor
Type of Resource:	text
Date Issued:	2004
Publisher:	University of Central Florida
Language(s):	English
Abstract/Description:	This study intends to build up new techniques for how to obtain completely data-driven choices of the smoothing parameter in functional estimation, within the confines of minimal assumptions. The focus of the study will be within the framework of the estimation of the distribution function, the density function and their multivariable extensions along with some of their functionals such as the location and the integrated squared derivatives.
Identifier:	CFE0000307 (IID), ucf:46314 (fedora)
Note(s):	2004-12-01 Ph.D. Arts and Sciences, Department of Mathematics Doctorate This record was generated from author submitted information.
Subject(s):	Kernel method Smoothing parameter selection Density fuction Distribution function Multivariate function estimation
Persistent Link to This Record:	http://purl.flvc.org/ucf/fd/CFE0000307
Restrictions on Access:	public
Host Institution:	UCF

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