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On the use of transformations for modeling multidimensional heterogeneous data

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dc.contributorMittenthal, John
dc.contributorParton, Jason
dc.contributorPorter, Michael
dc.contributorWang, Qin
dc.contributorZhu, Xuwen
dc.contributor.advisorMelnykov, Volodymyr
dc.contributor.authorSarkar, Shuchismita
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2020-01-16T15:03:38Z
dc.date.available2020-01-16T15:03:38Z
dc.date.issued2019
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractThe objective of cluster analysis is to find distinct groups of similar observations. There are many algorithms in literature that can perform this task and among them model based clustering is one of the most flexible tools. Assumption of Gaussian density for mixture components is quite popular in this field of study due to it’s convenient form. However, this assumption is not always valid. This thesis explores the use of various transformations for finding clusters in heterogeneous data. In this process, the thesis also attends to several data structures such as vector-, matrix-, tensor-, and network-valued data. In the first chapter, linear and non-linear transformations are used to model heterogeneous vector-valued observations when the data suffer from measurement inconsistency. The second chapter discusses an extensive set of parsimonious models for matrix-valued data. In the third chapter a methodology for clustering skewed tensor-valued data is developed and it is applied for analyzing remuneration of professors in American universities. The fourth chapter focuses on network-valued data and a novel finite mixture model addressing the dependent structure of network data is proposed. Finally, the fifth chapter describes the functionality of a R package “netClust” developed by the author for clustering unilayer and multilayer networks following the methodology proposed in Chapter four.en_US
dc.format.extent172 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0003407
dc.identifier.otherSarkar_alatus_0004D_13896
dc.identifier.urihttp://ir.ua.edu/handle/123456789/6464
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectStatistics
dc.subjectComputer science
dc.subjectMathematics
dc.titleOn the use of transformations for modeling multidimensional heterogeneous dataen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Information Systems, Statistics, and Management Science
etdms.degree.disciplineApplied Statistics
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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