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Article: A multivariate normal plot to detect nonnormality

TitleA multivariate normal plot to detect nonnormality
Authors
KeywordsHigh-dimensional data
Multivariate normality
Principal component
Spherical distribution
Issue Date2009
PublisherAmerican Statistical Association.
Citation
Journal Of Computational And Graphical Statistics, 2009, v. 18 n. 1, p. 53-72 How to Cite?
AbstractRoyston proposed a normal probability plot to detect nonnormality of univariate data. The normal probability plot was provided with normalized acceptance regions to enhance its interpretability. By using the theory of spherical distributions and the idea of principal component analysis, we propose an approach to extending Royston's normal plot to detecting nonmultivariate normality in analyzing high-dimensional data. The performance of the proposed multivariate normal plot is demonstrated by Monte Carlo studies and illustrated by two real datasets. © 2009 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/129033
ISSN
2015 Impact Factor: 1.755
2015 SCImago Journal Rankings: 2.321
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
The University of New Haven
Funding Information:

This research was supported by a University of Hong Kong research grant and The University of New Haven 2006 and 2007 Summer Faculty Fellowships.

References

 

DC FieldValueLanguage
dc.contributor.authorLiang, Jen_HK
dc.contributor.authorNg, KWen_HK
dc.date.accessioned2010-12-21T06:40:29Z-
dc.date.available2010-12-21T06:40:29Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Computational And Graphical Statistics, 2009, v. 18 n. 1, p. 53-72en_HK
dc.identifier.issn1061-8600en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129033-
dc.description.abstractRoyston proposed a normal probability plot to detect nonnormality of univariate data. The normal probability plot was provided with normalized acceptance regions to enhance its interpretability. By using the theory of spherical distributions and the idea of principal component analysis, we propose an approach to extending Royston's normal plot to detecting nonmultivariate normality in analyzing high-dimensional data. The performance of the proposed multivariate normal plot is demonstrated by Monte Carlo studies and illustrated by two real datasets. © 2009 American Statistical Association.en_HK
dc.languageeng-
dc.publisherAmerican Statistical Association.-
dc.relation.ispartofJournal of Computational and Graphical Statisticsen_HK
dc.subjectHigh-dimensional dataen_HK
dc.subjectMultivariate normalityen_HK
dc.subjectPrincipal componenten_HK
dc.subjectSpherical distributionen_HK
dc.titleA multivariate normal plot to detect nonnormalityen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1061-8600&volume=18&issue=1&spage=52&epage=72&date=2009&atitle=A+multivariate+normal+plot+to+detect+nonnormality-
dc.identifier.emailNg, KW: kaing@hkucc.hku.hken_HK
dc.identifier.authorityNg, KW=rp00765en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/jcgs.2009.0004en_HK
dc.identifier.scopuseid_2-s2.0-64849093535en_HK
dc.identifier.hkuros170818-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-64849093535&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue1en_HK
dc.identifier.spage53en_HK
dc.identifier.epage72en_HK
dc.identifier.isiWOS:000268506000004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLiang, J=7404541870en_HK
dc.identifier.scopusauthoridNg, KW=7403178774en_HK

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