Defense Date

5-27-2005

Graduation Date

Summer 2005

Availability

Immediate Access

Submission Type

thesis

Degree Name

MS

Department

Computational Mathematics

School

McAnulty College and Graduate School of Liberal Arts

Committee Chair

John C. Kern

Committee Member

Frank D'Amico

Committee Member

Kathleen Taylor

Keywords

Bayesian, Hierarchical Model, Longitudinal Frequency Data

Abstract

This research is to develop a longitudinal frequency model for data collected regularly for several individuals over an extended time period. This model must recognize explicitly the discrete nature of the data, as well as any dependence that exists among an individual's time consecutive measurements. Motivated by a study investigating alternative treatments for relief of menopausal symptoms, we apply this model to actual study data in an effort to compare treatment effectiveness. We propose a Bayesian hierarchical model to describe not only frequency measurements, but also the parameters that govern an individual profile.

Format

PDF

Language

English

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