{"id":{"repo_id":"duquesne","oai_identifier":"oai:dsc.duq.edu:etd-1727"},"canonical_url":"https://search.dev.ndltd.org/etd/duquesne/oai:dsc.duq.edu:etd-1727","repository":{"repo_id":"duquesne","name":"Duquesne","base_url":"https://dsc.duq.edu/do/oai/"},"display":{"title":"Bayesian Hierarchical Modeling for Longitudinal Frequency Data","abstract":"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. 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We propose a Bayesian hierarchical model to describe not only frequency measurements, but also the parameters that govern an individual profile.","abstract_has_math":false,"creators":["Jordan, Joseph Patrick"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["John C. 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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."]},{"key":"dc:title","label":"Title","values":["Bayesian Hierarchical Modeling for Longitudinal Frequency Data"]}]}],"canonical_facts":{"dc:contributor":["John C. Kern","Frank D'Amico","Kathleen Taylor"],"dc:creator":["Jordan, Joseph Patrick"],"dc:date.available":["2018-08-03T07:00:00Z"],"dc:description.abstract":["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."],"dc:identifier":["https://dsc.duq.edu/etd/711"],"dc:language":["English"],"dc:subject":["Bayesian","Hierarchical Model","Longitudinal Frequency Data"],"dc:title":["Bayesian Hierarchical Modeling for Longitudinal Frequency Data"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:54Z"}