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

• Main Author: Michels, Kurt Andrew
• Other Authors: Zhang, Hao Helen
• Language: en_US
• Published: The University of Arizona. 2016
• Subjects: Forward Regression; Genome Wide Association Study; Group Data; Interactions; R; Statistics; Big Data
• Online Access: http://hdl.handle.net/10150/613247; http://arizona.openrepository.com/arizona/handle/10150/613247

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.