A Piecewise Linear Generalized Poisson Regression Approach to Modeling Longitudinal Frequency Data

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</p><p> 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</p><p> univariate data is also provided.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2004

Author dc:creator

• Borgesi, Jennifer

Contributors dc:contributor

• John C. Kern
• Constance D. Ramirez
• Frank D'Amico
• Kathleen Taylor

• bayesian
• generalized poisson
• longitudinal
• mcmc
• piecewise linear

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/341

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1354