Keywords
Kernel method, Smoothing parameter selection, Density fuction, Distribution function, Multivariate function estimation
Abstract
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.
Notes
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Graduation Date
2004
Semester
Fall
Advisor
Ahmad, Ibrahim
Degree
Doctor of Philosophy (Ph.D.)
College
College of Arts and Sciences
Department
Mathematics
Degree Program
Mathematics
Format
application/pdf
Identifier
CFE0000307
URL
http://purl.fcla.edu/fcla/etd/CFE0000307
Language
English
Release Date
December 2004
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Amezziane, Mohamed, "Smoothing Parameter Selection In Nonparametric Functional Estimation" (2004). Electronic Theses and Dissertations. 160.
https://stars.library.ucf.edu/etd/160