de Leon, Alexander R.Raz, Saifa2016-09-062016-09-0620162016Raz, S. (2016). COM-Poisson Clustering of Correlated Bivariate Over- and Under-Dispersed Counts (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25384http://hdl.handle.net/11023/3263Ruan (2015) recently proposed using a finite mixture model with components modelled by Gaussian copula with Poisson margins (BP-GCD) as the basis for model-based clustering of bivariate correlated counts. Although the Poisson distribution is a useful model for modelling count data, the distribution is constrained by its equi-dispersion assumption. Motivated by this limitation, the thesis introduces a more flexible model, one with Gaussian copula models as components but with Conway-Maxwell Poisson (COM-Poisson) margins (BCOM-GCD) which allows the accounting of under- and over-dispersion in the correlated count data. We test our proposed method on a variety of simulated settings and on data from the Australian National Health Survey to explore the impact of ignoring the non-equidispersion. Our simulations and real-life data analysis indicate that using BCOM-GCDs as mixture components instead of BP-GCDs provides a better and more flexible approach for performing model-based clustering for under- or over-dispersed counts.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.StatisticsGaussian copulamaster thesis10.11575/PRISM/25384