Author

Todd Buretz

Publication Date

2017

Document Type

Dissertation/Thesis

First Advisor

Polansky, Alan M.

Degree Name

M.S. (Master of Science)

Department

Department of Statistics

LCSH

Statistics

Abstract

Functional data analysis (FDA) is an important technique that allows discrete data to be treated as continuous. Using FDA, data can be smoothed in order capture specific patterns as well as leaving out unwanted noise which aids in analysis. In order to smooth physical activity data, leave-one-out cross-validation can be used to calculate the smoothing parameter for each individual subject. Pseudo-data is then generated using the bootstrap resampling technique. The mean functions from the pseudo-data as well as the observed data are then compared against each other to calculate the significance of the mean functional profiles for males and females.

Comments

Advisors: Alan M. Polansky.||Committee members: Nader Ebrahimi; Duchwan Ryu.||Includes bibliographical references.||Includes illustrations.

Extent

v, 62 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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