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Mixtures of Skew-t Factor Analyzers

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dc.contributor.advisor McNicholas, Paul Murray, Paula 2013-01-10T14:12:28Z 2013-01-10T14:12:28Z 2012-11 2012-11-30 2012-11-01
dc.description.abstract Model-based clustering allows for the identification of subgroups in a data set through the use of finite mixture models. When applied to high-dimensional microarray data, we can discover groups of genes characterized by their gene expression profiles. In this thesis, a mixture of skew-t factor analyzers is introduced for the clustering of high-dimensional data. Notably, we make use of a version of the skew-t distribution which has not previously appeared in mixture-modelling literature. Allowing a constraint on the factor loading matrix leads to two mixtures of skew-t factor analyzers models. These models are implemented using the alternating expectation-conditional maximization algorithm for parameter estimation with an Aitken's acceleration stopping criterion used to determine convergence. The Bayesian information criterion is used for model selection and the performance of each model is assessed using the adjusted Rand index. The models are applied to both real and simulated data, obtaining clustering results which are equivalent or superior to those of established clustering methods. en_US
dc.language.iso en en_US
dc.subject Cluster Analysis en_US
dc.subject Model-based Clustering en_US
dc.subject Skew-t Distribution en_US
dc.subject Factor Analysis en_US
dc.title Mixtures of Skew-t Factor Analyzers en_US
dc.type Thesis en_US Bioinformatics en_US Master of Science en_US Department of Mathematics and Statistics en_US
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