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SMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION
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TitleSMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION
AuthorAmezziane, Mohamed
KeywordsKernel method
Smoothing parameter selection
Density fuction
Distribution function
Multivariate function estimation
AbstractThis 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.
AdviserAhmad, Ibrahim
PublisherUniversity of Central Florida
DegreePh.D.
Degree DisciplineDepartment of Mathematics
Degree GrantorArts and Sciences
Degree ProgramMathematics
Graduation Date2004-12-01
TypeDoctoral dissertation
Access LevelPublic - Allow Worldwide Access
Release Date2004-12-01
RepositoryUniversity Archives
Repository CollectionElectronic Theses and Dissertations
IdentifierCFE0000307
Access Linkhttp://purl.fcla.edu/fcla/etd/CFE0000307

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