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Article: A test for symmetries of multivariate probability distributions

TitleA test for symmetries of multivariate probability distributions
Authors
KeywordsConditional Monte Carlo test
Distance-based test statistic
Multivariate symmetry
Significance testing
Issue Date1999
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 1999, v. 86 n. 3, p. 605-614 How to Cite?
AbstractA Monte Carlo test for multivariate symmetries is proposed. The Monte Carlo simulations are performed conditionally on a minimal sufficient statistic for the class of distributions with symmetric density. Additionally, a general purpose test statistic based on a distance measure between the probability density function and its symmetrised version is introduced. The Monte Carlo tests for spherical symmetry and multivariate reflection symmetry are studied numerically for this statistic and the results indicate that the tests perform well compared to other tests. The method is illustrated with an analysis of a real dataset.
Persistent Identifierhttp://hdl.handle.net/10722/224683
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801

 

DC FieldValueLanguage
dc.contributor.authorDiks, C-
dc.contributor.authorTong, H-
dc.date.accessioned2016-04-12T03:12:04Z-
dc.date.available2016-04-12T03:12:04Z-
dc.date.issued1999-
dc.identifier.citationBiometrika, 1999, v. 86 n. 3, p. 605-614-
dc.identifier.issn0006-3444-
dc.identifier.urihttp://hdl.handle.net/10722/224683-
dc.description.abstractA Monte Carlo test for multivariate symmetries is proposed. The Monte Carlo simulations are performed conditionally on a minimal sufficient statistic for the class of distributions with symmetric density. Additionally, a general purpose test statistic based on a distance measure between the probability density function and its symmetrised version is introduced. The Monte Carlo tests for spherical symmetry and multivariate reflection symmetry are studied numerically for this statistic and the results indicate that the tests perform well compared to other tests. The method is illustrated with an analysis of a real dataset.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/-
dc.relation.ispartofBiometrika-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here]. -
dc.subjectConditional Monte Carlo test-
dc.subjectDistance-based test statistic-
dc.subjectMultivariate symmetry-
dc.subjectSignificance testing-
dc.titleA test for symmetries of multivariate probability distributions-
dc.typeArticle-
dc.identifier.emailTong, H: howell.tong@gmail.com-
dc.identifier.doi10.1093/biomet/86.3.605-
dc.identifier.hkuros47824-
dc.identifier.volume86-
dc.identifier.issue3-
dc.identifier.spage605-
dc.identifier.epage614-
dc.publisher.placeUnited Kingdom-

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