Multivariate nonparametric tests of independence

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dc.contributor.author Taskinen, Sara
dc.contributor.author Randles, Ronald
dc.contributor.author Oja, Hannu
dc.date.accessioned 2012-12-03T08:51:49Z
dc.date.available 2012-12-03T08:51:49Z
dc.date.issued 2005 fi
dc.identifier.citation Taskinen, S., Randles, R., & Oja, H. (2005). Multivariate nonparametric tests of independence. Journal of the American Statistical Association, 100 (471), 916-925. doi:10.1198/016214505000000097 fi
dc.identifier.issn 0162-1459
dc.identifier.other TUTKAID_18238
dc.identifier.uri http://hdl.handle.net/123456789/40502
dc.description.abstract New 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.iso eng
dc.publisher American Statistical Association
dc.relation.ispartof Journal of the American Statistical Association
dc.rights © 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.subject.other riippumattomuus
dc.subject.other affine invariance
dc.subject.other Kendall's tau
dc.subject.other Pitman efficiency
dc.subject.other Quadrant test
dc.subject.other Robustness
dc.subject.other Spearman's rho
dc.title Multivariate nonparametric tests of independence
dc.type Article en
dc.identifier.urn URN:NBN:fi:jyu-201211293122
dc.contributor.laitos Matematiikan ja tilastotieteen laitos fi
dc.contributor.laitos Department of Mathematics and Statistics en
jyx.tutka.volyme 100
jyx.tutka.mnumber 471,
jyx.tutka.pagetopage 916-925.
dc.type.uri http://purl.org/eprint/type/SubmittedJournalArticle
dc.identifier.doi 10.1198/016214505000000097
dc.date.updated 2012-11-29T10:39:54Z
dc.description.version Author's Final draft
eprint.status http://purl.org/eprint/type/status/PeerReviewed

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