Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/20792
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | McNicholas, Paul D. | - |
dc.contributor.author | Zhang, Xu Xuan | - |
dc.date.accessioned | 2016-11-10T18:14:57Z | - |
dc.date.available | 2016-11-10T18:14:57Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/11375/20792 | - |
dc.description.abstract | The Gaussian mixture model has been used for model-based clustering analysis for decades. Most model-based clustering analyses are based on the Gaussian mixture model. Model averaging approaches for Gaussian mixture models are proposed by Wei and McNicholas, based on a family of 14 Gaussian parsimonious clustering models. In this thesis, we use non-Gaussian mixture models, namely the tEigen family, for our averaging approaches. This paper studies fitting in an averaged model from a set of multivariate t-mixture models instead of fitting a best model. | en_US |
dc.language.iso | en | en_US |
dc.subject | Model-based Clustering, Model Averaging, Mixture Models | en_US |
dc.title | Non-Gaussian Mixture Model Averaging for 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 | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_XuXuan_2016Nov_MSc.pdf | 274.16 kB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.