Date of Award

2011

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Mathematics and Statistics

First Advisor

Abdul Hussein

Keywords

Applied sciences, Pure sciences

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Monitoring changes in health care performances, financial markets, and industrial processes has recently gained momentum due to increased capability of computers and the availability of real-time data collection and storage software. As a consequence, there has been a growing demand in developing statistically rigorous methodologies for monitoring and change-point detection. In many practical situations, the data being monitored for the purpose of detecting changes, present serial correlations. Hussein (2011) is currently working on a new statistical procedure for monitoring changes in the coefficients of logistic regression model with AR( p)-type structure. The objective of this thesis is (a) to use Monte Carlo experiments to evaluate the average stopping times, probability of false alarm, and power of the proposed procedure; (b) to illustrate the usefulness of the method by using an IBM stock transactions data as well as data on rainfall.

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