Under realistic scenarios, data are often incomplete, asymmetric, or of high-dimensionality.
More intricate data structures often render standard approaches infeasible due to
methodological or computational limitations. This monograph consists of four contributions each solving a specific problem within model-based clustering. An R package
is developed consisting of a three-phase imputation method for both elliptical and hyperbolic parsimonious models. A novel stochastic technique is employed to speed up
computations for hyperbolic distributions demonstrating superior performance overall. A hyperbolic transformation model is conceived for clustering asymmetrical data
within a heterogeneous context. Finally, for high-dimensionality, a framework is developed for assessing matrix variate normality within three-way datasets. All things
considered, this work constitutes a powerful set of tools to deal with the ever-growing
complexity of big data / Dissertation / Doctor of Science (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28448 |
| Date | January 2023 |
| Creators | Pocuca, Nikola |
| Contributors | McNicholas, Paul, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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