Model-based Learning: t-Families, Variable Selection, and Parameter Estimation

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Model-based Learning: t-Families, Variable Selection, and Parameter Estimation

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dc.contributor.advisor McNicholas, Paul
dc.contributor.author Andrews, Jeffrey Lambert
dc.date 2012-07-25
dc.date.accessioned 2012-08-27T19:26:18Z
dc.date.available 2012-08-27T19:26:18Z
dc.date.issued 2012-08-27
dc.identifier.uri http://hdl.handle.net/10214/3879
dc.description.abstract The phrase model-based learning describes the use of mixture models in machine learning problems. This thesis focuses on a number of issues surrounding the use of mixture models in statistical learning tasks: including clustering, classification, discriminant analysis, variable selection, and parameter estimation. After motivating the importance of statistical learning via mixture models, five papers are presented. For ease of consumption, the papers are organized into three parts: mixtures of multivariate t-families, variable selection, and parameter estimation. en_US
dc.description.sponsorship Natural Sciences and Engineering Research Council of Canada through a doctoral postgraduate scholarship. en_US
dc.language.iso en en_US
dc.subject Computational Statistics en_US
dc.subject Cluster Analysis en_US
dc.subject Multivariate Statistics en_US
dc.subject Classification en_US
dc.subject Statistical Learning en_US
dc.subject Mixture Models en_US
dc.title Model-based Learning: t-Families, Variable Selection, and Parameter Estimation en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Doctor of Philosophy en_US
dc.degree.department Department of Mathematics and Statistics en_US


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