Return to search

Hyperbolic Distributions and Transformations for Clustering Incomplete Data with Extensions to Matrix Variate Normality

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)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28448
Date January 2023
CreatorsPocuca, Nikola
ContributorsMcNicholas, Paul, Mathematics and Statistics
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

Page generated in 0.0016 seconds