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dc.contributor.authorTaskinen, Sara
dc.contributor.authorRandles, Ronald
dc.contributor.authorOja, Hannu
dc.date.accessioned2012-12-03T08:51:49Z
dc.date.available2012-12-03T08:51:49Z
dc.date.issued2005fi
dc.identifier.citationTaskinen, S., Randles, R., & Oja, H. (2005). Multivariate nonparametric tests of independence. <em>Journal of the American Statistical Association</em>, 100 (471), 916-925. <a href="http://dx.doi.org/10.1198/016214505000000097">doi:10.1198/016214505000000097</a>fi
dc.identifier.otherTUTKAID_18238
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/40502
dc.description.abstractNew test statistics are proposed for testing whether two random vectors are independent. Gieser and Randles, as well as Taskinen, Kankainen, and Oja have introduced and discussed multivariate extensions of the quadrant test of Blomqvist. This article serves as a sequel to this work and presents new multivariate extensions of Kendall's tau and Spearman's rho statistics. Two different approaches are discussed. First, interdirection proportions are used to estimate the cosines of angles between centered observation vectors and between differences of observation vectors. Second, covariances between affine-equivariant multivariate signs and ranks are used. The test statistics arising from these two approaches appear to be asymptotically equivalent if each vector is elliptically symmetric. The spatial sign versions are easy to compute for data in common dimensions, and they provide practical, robust alternatives to normal-theory methods. Asymptotic theory is developed to approximate the finite-sample null distributions as well, as to calculate limiting Pitman efficiencies. Small-sample null permutation distributions are also described. A simple simulation study is used to compare the proposed tests with the classical Wilks test. Finally, the theory is illustrated by an example.fi
dc.language.isoeng
dc.publisherAmerican Statistical Association
dc.relation.ispartofseriesJournal of the American Statistical Association
dc.subject.otherriippumattomuus
dc.subject.otheraffine invariance
dc.subject.otherKendall's tau
dc.subject.otherPitman efficiency
dc.subject.otherQuadrant test
dc.subject.otherRobustness
dc.subject.otherSpearman's rho
dc.titleMultivariate nonparametric tests of independence
dc.typeArticle
dc.identifier.urnURN:NBN:fi:jyu-201211293122
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/SubmittedJournalArticle
dc.identifier.doi10.1198/016214505000000097
dc.date.updated2012-11-29T10:39:54Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange916-925.
dc.relation.issn0162-1459
dc.relation.numberinseries471
dc.relation.volume100
dc.type.versionacceptedVersion
dc.rights.copyright© American Statistical Association. This is an author's final draft version of an article whose final and definitive form has been published by American Statistical Association.
dc.rights.accesslevelopenAccessfi


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