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New statistical methods and computat...

Michels, Kurt A.

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New statistical methods and computational tools for mining big data, with applications in plant sciences.

Record Type:	Electronic resources : Monograph/item
Title/Author:	New statistical methods and computational tools for mining big data, with applications in plant sciences./
Author:	Michels, Kurt A.
Description:	175 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10111588
ISBN:	9781339747972

New statistical methods and computational tools for mining big data, with applications in plant sciences.
Michels, Kurt A.

New statistical methods and computational tools for mining big data, with applications in plant sciences. - 175 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--The University of Arizona, 2016.

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.

ISBN: 9781339747972Subjects--Topical Terms:

517247
Statistics.

New statistical methods and computational tools for mining big data, with applications in plant sciences.
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245 1 0 $a New statistical methods and computational tools for mining big data, with applications in plant sciences.
300 $a 175 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
500 $a Adviser: Hao Zhang.
502 $a Thesis (Ph.D.)--The University of Arizona, 2016.
520 $a 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.
590 $a School code: 0009.
650 4 $a Statistics. $3 517247
650 4 $a Computer science. $3 523869
650 4 $a Biostatistics. $3 1002712
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710 2 $a The University of Arizona. $b Statistics. $3 3180378
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10111588