Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data

Handle URI:
http://hdl.handle.net/10754/578818
Title:
Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data
Authors:
Dong, Kai; Pang, Herbert; Tong, Tiejun; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
DNA sequencing techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the “large pp small nn” paradigm, the traditional Hotelling’s T2T2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling’s test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of pp and nn for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when nn is moderate or large, but it is better when nn is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling’s test.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data 2015 Journal of Multivariate Analysis
Publisher:
Elsevier BV
Journal:
Journal of Multivariate Analysis
Issue Date:
16-Sep-2015
DOI:
10.1016/j.jmva.2015.08.022
Type:
Article
ISSN:
0047259X
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0047259X15002146
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDong, Kaien
dc.contributor.authorPang, Herberten
dc.contributor.authorTong, Tiejunen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-09-28T13:48:44Zen
dc.date.available2015-09-28T13:48:44Zen
dc.date.issued2015-09-16en
dc.identifier.citationShrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data 2015 Journal of Multivariate Analysisen
dc.identifier.issn0047259Xen
dc.identifier.doi10.1016/j.jmva.2015.08.022en
dc.identifier.urihttp://hdl.handle.net/10754/578818en
dc.description.abstractDNA sequencing techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the “large pp small nn” paradigm, the traditional Hotelling’s T2T2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling’s test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of pp and nn for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when nn is moderate or large, but it is better when nn is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling’s test.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0047259X15002146en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 16 September 2015. DOI: 10.1016/j.jmva.2015.08.022en
dc.subjectDiagonal Hotelling’s testen
dc.subjectHigh-dimensional dataen
dc.subjectMicroarray dataen
dc.subjectNull distributionen
dc.subjectOptimal variance estimationen
dc.titleShrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size dataen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Multivariate Analysisen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Mathematics, Hong Kong Baptist University, Hong Kongen
dc.contributor.institutionSchool of Public Health, The University of Hong Kong, Hong Kongen
dc.contributor.institutionDepartment of Biostatistics and Bioinformatics, Duke University, USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorGenton, Marc G.en
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