Kernel method, Smoothing parameter selection, Density fuction, Distribution function, Multivariate function estimation
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.
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Doctor of Philosophy (Ph.D.)
College of Arts and Sciences
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Amezziane, Mohamed, "Smoothing Parameter Selection In Nonparametric Functional Estimation" (2004). Electronic Theses and Dissertations, 2004-2019. 160.