De Leon, Alexander R.Wu, JingjingBurak, Katherine L.2019-06-142019-06-142019-06-12Burak, K. L. (2019). Cluster analysis of correlated non-Gaussian continuous data via finite mixtures of Gaussian copula distributions (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110497Model-based cluster analysis in non-Gaussian settings is not straightforward due to a lack of standard models for non-Gaussian data. In this thesis, we adopt the class of Gaussian copula distributions (GCDs) to develop a flexible model-based clustering methodology that can accommodate a variety of correlated, non-Gaussian continuous data, where variables may have different marginal distributions and come from different parametric families. Unlike conventional model-based approaches that rely on the assumption of conditional independence, GCDs model conditional dependence among the disparate variables using the matrix of so-called normal correlations. We outline a hybrid approach to cluster analysis that combines the method of inference functions for margins (IFM) and the parameter-expanded EM (PX-EM) algorithm. We then report simulation results to investigate the performance of our methodology. Finally, we highlight the applications of this research by applying this methodology to a dataset regarding the purchases made by clients of a wholesale distributor.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.Cluster analysisCopulaStatisticsmaster thesis10.11575/PRISM/36637