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Variable Selection for High Dimensio...

Northwestern University.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Variable Selection for High Dimensional Compositional Data with Application in Metagenomics.
作者:	Wang, Pan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:	98 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
附註:	Publisher info.: Dissertation/Thesis.
附註:	Advisor: Jiang, Hongmei.
Contained By:	Dissertations Abstracts International80-10B.
標題:	Statistics.
電子資源:	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:

182057
Statistics.

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