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http://hdl.handle.net/11375/20693
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Paul D | - |
dc.contributor.author | Cheam, Amay SM | - |
dc.date.accessioned | 2016-10-18T20:16:59Z | - |
dc.date.available | 2016-10-18T20:16:59Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://hdl.handle.net/11375/20693 | - |
dc.description.abstract | Finite mixture models have had a profound impact on the history of statistics, contributing to modelling heterogeneous populations, generalizing distributional assumptions, and lately, presenting a convenient framework for classification and clustering. A novel approach, via Gaussian mixture distribution, is introduced for modelling receiver operating characteristic curves. The absence of a closed-form for a functional form leads to employing the Monte Carlo method. This approach performs excellently compared to the existing methods when applied to real data. In practice, the data are often non-normal, atypical, or skewed. It is apparent that non-Gaussian distributions be introduced in order to better fit these data. Two non-Gaussian mixtures, i.e., t distribution and skew t distribution, are proposed and applied to real data. A novel mixture is presented to cluster spatial and temporal data. The proposed model defines each mixture component as a mixture of autoregressive polynomial with logistic links. The new model performs significantly better compared to the most well known model-based clustering techniques when applied to real data. | en_US |
dc.language.iso | en | en_US |
dc.subject | Finite mixture models | en_US |
dc.subject | ROC curve | en_US |
dc.subject | Spatio-temporal data | en_US |
dc.subject | Functional data | en_US |
dc.subject | Model-based clustering | en_US |
dc.subject | EM algorithm | en_US |
dc.title | Mixture models for ROC curve and spatio-temporal clustering | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Cheam_Amay_SM_201609_PhD.pdf | 1.42 MB | Adobe PDF | View/Open |
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