McAnulty College and Graduate School of Liberal Arts
John C. Kern
Bayesian, Hierarchical Model, 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.
Jordan, J. (2005). Bayesian Hierarchical Modeling for Longitudinal Frequency Data (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/711