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

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    Author
    Michels, Kurt Andrew
    Issue Date
    2016
    Keywords
    Forward Regression
    Genome Wide Association Study
    Group Data
    Interactions
    R
    Statistics
    Big Data
    Advisor
    Zhang, Hao Helen
    
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    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
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Statistics
    Degree Grantor
    University of Arizona
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