Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data

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atmire.migration.oldid3728
dc.contributor.advisorde Leon, Alexander
dc.contributor.authorYeasmin, Fahmida
dc.date.accessioned2015-09-29T18:47:29Z
dc.date.available2015-11-20T08:00:42Z
dc.date.issued2015-09-29
dc.date.submitted2015en
dc.description.abstractSerially dependent multivariate longitudinal non-Gaussian outcome data are commonly encountered in many fields of study, especially in biomedical sciences, finance, and so on. However, flexible methodologies for joint analysis of these outcomes are not well developed. Recently, Wu and de Leon (2014) and Withanage and de Leon (2015) introduced the class of Gaussian copula mixed models (GCMMs) for joint analysis of non-Gaussian outcomes. We adapt and extend the GCMM to settings that involve conditional as well as serial dependencies among longitudinal observations on the same or on different outcomes. We investigate the impact of failing to account for these dependencies via simulations. We illustrate our methodology on two datasets: one on data obtained from primary biliary cirrhosis patients, and the other on data from the Iowa Youth and Families Project.en_US
dc.identifier.citationYeasmin, F. (2015). Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24822en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/24822
dc.identifier.urihttp://hdl.handle.net/11023/2547
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectStatistics
dc.subject.classificationMultivariateen_US
dc.subject.classificationLongitudinalen_US
dc.subject.classificationNon-Gaussianen_US
dc.subject.classificationCopulaen_US
dc.titleAnalysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data
dc.typemaster thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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