New Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciences

Persistent Link:
http://hdl.handle.net/10150/613247
Title:
New Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciences
Author:
Michels, Kurt Andrew
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The purpose of this dissertation is to develop new statistical tools for mining big data in plant sciences. In particular, the dissertation consists of four inter-related projects to address various methodological and computational challenges in phylogenetic methods. Project 1 aims to systematically test different optimization tools and provide useful strategies to improve optimization in practice. Project 2 develops a new R package rPlant, which provides a friendly and convenient toolbox for users of iPlant. Project 3 presents a fast and effective group-screening method to identify important genetic factors in GWAS, with theoretical justifications and nice asymptotic properties. Project 4 develops a new statistical tool to identify gene-gene interactions, with the ability of handling the interactions between groups of covariates.
Type:
text; Electronic Dissertation
Keywords:
Forward Regression; Genome Wide Association Study; Group Data; Interactions; R; Statistics; Big Data
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Statistics
Degree Grantor:
University of Arizona
Advisor:
Zhang, Hao Helen

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleNew Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciencesen_US
dc.creatorMichels, Kurt Andrewen
dc.contributor.authorMichels, Kurt Andrewen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractThe purpose of this dissertation is to develop new statistical tools for mining big data in plant sciences. In particular, the dissertation consists of four inter-related projects to address various methodological and computational challenges in phylogenetic methods. Project 1 aims to systematically test different optimization tools and provide useful strategies to improve optimization in practice. Project 2 develops a new R package rPlant, which provides a friendly and convenient toolbox for users of iPlant. Project 3 presents a fast and effective group-screening method to identify important genetic factors in GWAS, with theoretical justifications and nice asymptotic properties. Project 4 develops a new statistical tool to identify gene-gene interactions, with the ability of handling the interactions between groups of covariates.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectForward Regressionen
dc.subjectGenome Wide Association Studyen
dc.subjectGroup Dataen
dc.subjectInteractionsen
dc.subjectRen
dc.subjectStatisticsen
dc.subjectBig Dataen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorZhang, Hao Helenen
dc.contributor.committeememberBillheimer, David D.en
dc.contributor.committeememberKececioglu, John D.en
dc.contributor.committeememberMerchant, Nirav C.en
dc.contributor.committeememberMatasci, Naimen
dc.contributor.committeememberZhang, Hao Helenen
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