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The development of diagnostic tools for mixture modeling and model-based clustering

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dc.contributorPerry, Marcus B.
dc.contributorBarrett, Bruce E.
dc.contributorPorter, Michael D.
dc.contributorWang, James L.
dc.contributor.advisorMelnykov, Volodymyr
dc.contributor.authorZhu, Xuwen
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-04-26T14:26:55Z
dc.date.available2017-04-26T14:26:55Z
dc.date.issued2016
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractCluster analysis performs unsupervised partition of heterogeneous data. It has applications in almost all fields of study. Model-based clustering is one of the most popular clustering methods these days due to its flexibility and interpretability. It is based on finite mixture models. However, the development of diagnostic tools and visualization tools for clustering procedures is limited. This dissertation is devoted to assessing different properties of the clustering procedure. This report has four chapters. The summary of each chapter is given below: In the first chapter we provide the practitioners with an approach to assess the certainty of a classification made in model-based clustering. The second chapter introduces a novel finite mixture model called Manly mixture model. It is capable of modeling skewness in data and performs diagnostics on the normality of variables. In the third chapter we develop an extension of the traditional K-means procedure that is capable of modeling skewness in data. The fourth chapter contributes to the ManlyMix R package, which is the developed software corresponding to our paper in Chapter 2.en_US
dc.format.extent160 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0002437
dc.identifier.otherZhu_alatus_0004D_12741
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3118
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.titleThe development of diagnostic tools for mixture modeling and model-based clusteringen_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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