New Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciences
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...
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| Language: | en_US |
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The University of Arizona.
2016
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| Online Access: | http://hdl.handle.net/10150/613247 http://arizona.openrepository.com/arizona/handle/10150/613247 |
| Summary: | 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. |
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