Bayesian Hierarchical Modeling for Longitudinal Frequency Data

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2005

Author dc:creator

• Jordan, Joseph Patrick

Contributors dc:contributor

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

• Bayesian
• Hierarchical Model
• Longitudinal Frequency Data

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/711

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1727