de Leon, AlexanderYeasmin, Fahmida2015-09-292015-11-202015-09-292015Yeasmin, 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/24822http://hdl.handle.net/11023/2547Serially 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.engUniversity 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.StatisticsMultivariateLongitudinalNon-GaussianCopulamaster thesis10.11575/PRISM/24822