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.","abstract_html":"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.","abstract_has_math":false,"creators":["Borgesi, Jennifer"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["John C. Kern","Constance D. Ramirez","Frank D'Amico","Kathleen Taylor"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-01-01T08:00:00Z","date_published":"2004-01-01T08:00:00Z","updated_at":"2026-07-24T02:09:20Z","subjects":["bayesian","generalized poisson","longitudinal","mcmc","piecewise linear"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/341","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John C. Kern","Constance D. Ramirez","Frank D'Amico","Kathleen Taylor"]},{"key":"dc:creator","label":"Author","values":["Borgesi, Jennifer"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["bayesian","generalized poisson","longitudinal","mcmc","piecewise linear"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/341"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["A Piecewise Linear Generalized Poisson Regression Approach to Modeling Longitudinal Frequency Data"]}]}],"canonical_facts":{"dc:contributor":["John C. Kern","Constance D. Ramirez","Frank D'Amico","Kathleen Taylor"],"dc:creator":["Borgesi, Jennifer"],"dc:date.available":["2018-08-03T07:00:00Z"],"dc:description.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."],"dc:identifier":["https://dsc.duq.edu/etd/341"],"dc:language":["English"],"dc:subject":["bayesian","generalized poisson","longitudinal","mcmc","piecewise linear"],"dc:title":["A Piecewise Linear Generalized Poisson Regression Approach to Modeling Longitudinal Frequency Data"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:20Z"}