Publication Date

2023

Document Type

Dissertation/Thesis

First Advisor

Ryu, Duchwan D.

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Mathematical Sciences

Abstract

Functional Data Analysis is often used in the study of data that exists over a continuum, such as time. There are two datasets that will be considered here. For the first study we have a dataset on the efficacy of a lobectomy in reduction or elimination of epileptic seizures in patients. After an initial analysis of the dataset from a multinomial model perspective, we found that there were outliers in our dataset. From there, we considered a Multinomial Mixture Model to aid in the detection of outliers. In our second dataset we are considering a social robotics dataset where the purpose is to aid in the integration of students from diverse backgrounds into US school systems. We use Univariate Functional Data Analysis on our dataset before moving on to consider Multivariate Functional Data Analysis with the addition of Seemingly Unrelated Regression to our analysis and estimation of our Bayesian Estimates.

Extent

308 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

Download

Share

COinS