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dc.contributor.authorChen, LAen_US
dc.contributor.authorWelsh, AHen_US
dc.date.accessioned2014-12-08T15:41:51Z-
dc.date.available2014-12-08T15:41:51Z-
dc.date.issued2002-10-01en_US
dc.identifier.issn0047-259Xen_US
dc.identifier.urihttp://dx.doi.org/10.1006/jmva.2001.2043en_US
dc.identifier.urihttp://hdl.handle.net/11536/28467-
dc.description.abstractWe introduce bivariate quantiles which are defined through the bivariate distribution function. This approach ensures that, unlike most multivariate medians or the multivariate M-quartiles, the bivariate quantiles satisfy an analogous property to that of the univariate quantiles in that they partition R-2 into sets with a specified probability content. The definition of bivariate quantiles leads naturally to the definition of quantities such as the bivariate median, bivariate extremes, the bivariate quantile curve, and the bivariate trimmed mean. We also develop asymptotic representations for the bivariate quantiles. (C) 2002 Elsevier Science (USA).en_US
dc.language.isoen_USen_US
dc.subjectbivariate extremeen_US
dc.subjectbivariate medianen_US
dc.subjectbivariate quantileen_US
dc.subjectbivariate quantile curveen_US
dc.subjectbivariate trimmed meanen_US
dc.titleDistribution-function-based bivariate quantilesen_US
dc.typeArticleen_US
dc.identifier.doi10.1006/jmva.2001.2043en_US
dc.identifier.journalJOURNAL OF MULTIVARIATE ANALYSISen_US
dc.citation.volume83en_US
dc.citation.issue1en_US
dc.citation.spage208en_US
dc.citation.epage231en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000178596700010-
dc.citation.woscount5-
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