Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/20693
Title: | Mixture models for ROC curve and spatio-temporal clustering |
Authors: | Cheam, Amay SM |
Advisor: | McNicholas, Paul D |
Department: | Mathematics and Statistics |
Keywords: | Finite mixture models;ROC curve;Spatio-temporal data;Functional data;Model-based clustering;EM algorithm |
Publication Date: | 2016 |
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. |
URI: | http://hdl.handle.net/11375/20693 |
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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