Defense Date

4-15-2004

Graduation Date

Spring 2004

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

Constance D. Ramirez

Committee Member

Frank D'Amico

Committee Member

Kathleen Taylor

Keywords

bayesian, generalized poisson, longitudinal, mcmc, piecewise linear

Abstract

In this research we consider experiments that generate longitudinal frequency data. Often times this data comes from two or more experimental groups. Experiments that yield such data are common in the medical field and are often designed with the purpose of ascertaining differences among

experimental groups. Standard modeling techniques, such as repeated measures ANOVA, are inadequate for application to longitudinal frequency data because they ignore the correlation between the measurements as well as the discrete nature of the data. We present a piecewise-linear, generalized Poisson regression model for longitudinal frequency data. Based on the generalized Poisson distribution, this model is flexible enough to allow for (and detect) underdispersion, equidispersion, or overdispersion in the data. We apply this model to frequency data collected from a clinical trial studying the symptoms of menopausal women. A simulation study that implements a generalized Poisson model for

univariate data is also provided.

Format

PDF

Language

English

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