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dc.contributor.advisorSzmigielski, Jacek
dc.creatorStack, Karly L 1992-
dc.date.accessioned2016-08-26T15:28:19Z
dc.date.available2016-08-26T15:28:19Z
dc.date.created2016-10
dc.date.issued2016-08-23
dc.date.submittedOctober 2016
dc.identifier.urihttp://hdl.handle.net/10388/7402
dc.description.abstractMultivariate 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.mimetypeapplication/pdf
dc.subjectWishart
dc.subjectsingular Wishart
dc.subjectanti-Wishart
dc.titleA Derivation of the Wishart and Singular Wishart Distributions
dc.typeThesis
dc.date.updated2016-08-26T15:28:19Z
thesis.degree.departmentMathematics and Statistics
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Saskatchewan
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.Sc.)
dc.type.materialtext
dc.contributor.committeeMemberSoteros, Chris
dc.contributor.committeeMemberSarty, Gordon
dc.contributor.committeeMemberSamei, Ebrahim


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