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http://hdl.handle.net/11375/22317
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | McNicholas, Paul | - |
dc.contributor.author | Matira, Kevin | - |
dc.date.accessioned | 2017-10-30T15:17:35Z | - |
dc.date.available | 2017-10-30T15:17:35Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/11375/22317 | - |
dc.description.abstract | Various approaches for discriminant analysis of longitudinal data are investigated, with some focus on model-based approaches. The latter are typically based on the modi ed Cholesky decomposition of the covariance matrix in a Gaussian mixture; however, non-Gaussian mixtures are also considered. Where applicable, the Bayesian information criterion is used to select the number of components per class. The various approaches are demonstrated on real and simulated data. | en_US |
dc.language.iso | en | en_US |
dc.subject | mixture models | en_US |
dc.subject | supervised learning | en_US |
dc.subject | longitudinal data | en_US |
dc.subject | classification | en_US |
dc.subject | statistical learning | en_US |
dc.title | Discriminant Analysis for Longitudinal Data | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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matira_kevin_2017August_MSc.pdf | 597.75 kB | Adobe PDF | View/Open |
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