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Variable Selection for High Dimensio...

Wang, Pan.

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Variable Selection for High Dimensional Compositional Data with Application in Metagenomics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Variable Selection for High Dimensional Compositional Data with Application in Metagenomics./
Author:	Wang, Pan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	98 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
Contained By:	Dissertations Abstracts International80-10B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13807140
ISBN:	9781392028308

Variable Selection for High Dimensional Compositional Data with Application in Metagenomics.
Wang, Pan.

Variable Selection for High Dimensional Compositional Data with Application in Metagenomics. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 98 p.

Source: Dissertations Abstracts International, Volume: 80-10, Section: B.

Thesis (Ph.D.)--Northwestern University, 2019.

This item must not be added to any third party search indexes.

The advent of next-generation sequencing technologies has greatly promoted the devel- opment of metagenomics, and the analysis of compositional dataset has a wide range of application in this area. Because of the constraint that the sum of species relative abun- dance being 1, many traditional and classical statistical methods cannot be applied to the compositional data directly. We propose an averaging approach to identify which OTUs are associated with a phenotype such as the body mass index or the disease status, by using pe- nalized regression and stability selection method. We also identify subgroups of patients who will have improved treatment effect based on the characterization of a small set of OTUs.

ISBN: 9781392028308Subjects--Topical Terms:

517247
Statistics.

Variable Selection for High Dimensional Compositional Data with Application in Metagenomics.
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100 1 $a Wang, Pan. $3 1265149
245 1 0 $a Variable Selection for High Dimensional Compositional Data with Application in Metagenomics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 98 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Jiang, Hongmei.
502 $a Thesis (Ph.D.)--Northwestern University, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a The advent of next-generation sequencing technologies has greatly promoted the devel- opment of metagenomics, and the analysis of compositional dataset has a wide range of application in this area. Because of the constraint that the sum of species relative abun- dance being 1, many traditional and classical statistical methods cannot be applied to the compositional data directly. We propose an averaging approach to identify which OTUs are associated with a phenotype such as the body mass index or the disease status, by using pe- nalized regression and stability selection method. We also identify subgroups of patients who will have improved treatment effect based on the characterization of a small set of OTUs.
590 $a School code: 0163.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a Northwestern University. $b Statistics. $3 1673808
773 0 $t Dissertations Abstracts International $g 80-10B.
790 $a 0163
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13807140