Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

12-2016

Committee Chair or Co-Chairs

Jeff R. Knisley

Committee Members

Christopher Wallace, Robert M. Price Jr.

Abstract

In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare their performance using forecast accuracy and an area under the curve score via simulated sine waves with various intensities of Gaussian noise and Standard & Poors 500 historical data. Overall, that MILR outperforms SLR is validated on both realistic and simulated data. Finally, some possible future directions of research are discussed.

Document Type

Thesis - Open Access

Copyright

Copyright by Xiang Liu.

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