On the use of transformations for modeling multidimensional heterogeneous data

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dc.contributor Mittenthal, John
dc.contributor Parton, Jason
dc.contributor Porter, Michael
dc.contributor Wang, Qin
dc.contributor Zhu, Xuwen
dc.contributor.advisor Melnykov, Volodymyr
dc.contributor.author Sarkar, Shuchismita
dc.date.accessioned 2020-01-16T15:03:38Z
dc.date.available 2020-01-16T15:03:38Z
dc.date.issued 2019
dc.identifier.other u0015_0000001_0003407
dc.identifier.other Sarkar_alatus_0004D_13896
dc.identifier.uri http://ir.ua.edu/handle/123456789/6464
dc.description Electronic Thesis or Dissertation
dc.description.abstract The 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.
dc.format.extent 172 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Statistics
dc.subject.other Computer science
dc.subject.other Mathematics
dc.title On the use of transformations for modeling multidimensional heterogeneous data
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Department of Information Systems, Statistics, and Management Science
etdms.degree.discipline Applied Statistics
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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