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. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20792 |
| Date | January 2017 |
| Creators | Zhang, Xu Xuan |
| Contributors | McNicholas, Paul D., Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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