dc.contributor.advisor Szmigielski, Jacek dc.creator Stack, Karly L 1992- dc.date.accessioned 2016-08-26T15:28:19Z dc.date.available 2016-08-26T15:28:19Z dc.date.created 2016-10 dc.date.issued 2016-08-23 dc.date.submitted October 2016 dc.identifier.uri http://hdl.handle.net/10388/7402 dc.description.abstract Multivariate statistical analysis is the area of statistics that is concerned with observations made on many variables. Determining how variables are related is a main objective in multivariate analysis. The covariance matrix is an essential part of understanding the dependence between variables. The distribution of the sample covariance matrix for a sample from a multivariate normal distribution, known as the Wishart distribution, is fundamental to multivariate statistical analysis. An important assumption of the well-known Wishart distribution is that the number of variables is smaller than the number of observations. In high-dimensions when the number of variables exceeds the number of observations, the Wishart matrix is singular and has a singular Wishart distribution. The purpose of this research is to rederive the Wishart and singular Wishart distributions and understand the mathematics behind each derivation. dc.format.mimetype application/pdf dc.subject Wishart dc.subject singular Wishart dc.subject anti-Wishart dc.title A Derivation of the Wishart and Singular Wishart Distributions dc.type Thesis dc.date.updated 2016-08-26T15:28:19Z thesis.degree.department Mathematics and Statistics thesis.degree.discipline Mathematics thesis.degree.grantor University of Saskatchewan thesis.degree.level Masters thesis.degree.name Master of Science (M.Sc.) dc.type.material text dc.contributor.committeeMember Soteros, Chris dc.contributor.committeeMember Sarty, Gordon dc.contributor.committeeMember Samei, Ebrahim
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