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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/613247 |
| Date | January 2016 |
| Creators | Michels, Kurt Andrew |
| Contributors | Zhang, Hao Helen, Billheimer, David D., Kececioglu, John D., Merchant, Nirav C., Matasci, Naim, Zhang, Hao Helen |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| 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. |
Page generated in 0.002 seconds