Date of Award

May 2016

Degree Type

Thesis

Degree Name

Master of Science

Department

Mathematics

First Advisor

Daniel Gervini

Committee Members

Wei Wei, Peter Hinow

Abstract

Finding low-dimensional approximations to high-dimensional data is one of

the most important topics in statistics, which has also multiple applications

in economics, engineering and science. One suggestion in the literature ,based

on kernel smoothing, is a non-linear generalization of principal components.

This kernel-based approach comes with several complications. Therefore the

purpose of this thesis is to provide an alternative based on spline smoothing

which produces more reliable results.

Included in

Mathematics Commons

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