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dc.contributor.authorCuadras, C.M. (Carlos Maria)
dc.date.accessioned2007-11-15T17:51:12Z
dc.date.available2007-11-15T17:51:12Z
dc.date.issued2006
dc.identifier.citationCuadras, C. M. (Carlos María). "The importance of being the upper bound in the bivariate family". SORT, 2006, Vol. 30, núm. 1
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2099/3788
dc.description.abstractAny bivariate cdf is bounded by the Fr ´echet-Hoeffding lower and upper bounds. We illustrate the importance of the upper bound in several ways. Any bivariate distribution can be written in terms of this bound, which is implicit in logit analysis and the Lorenz curve, and can be used in goodness-of-fit assesment. Any random variable can be expanded in terms of some functions related to this bound.The Bayes approach in comparing two proportions can be presented as the problem of choosing a parametric prior distribution which puts mass on the null hypothesis. Accepting this hypothesis is equivalent to reaching the upper bound. We also present some parametric families making emphasis on this bound.
dc.format.extent55-84
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2006, Vol. 30, Núm. 1 [January-June]
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.subject.otherDistribution (Probability theory)
dc.subject.otherMultivariate analysis
dc.subject.otherInference
dc.titleThe importance of being the upper bound in the bivariate family.
dc.typeArticle
dc.subject.lemacDistribució (Teoria de la probabilitat)
dc.subject.lemacAnàlisi multivariable
dc.subject.lemacInferència
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
dc.subject.amsClassificació AMS::62 Statistics::62H Multivariate analysis
dc.subject.amsClassificació AMS::62 Statistics::62F Parametric inference
dc.rights.accessOpen Access


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 2.5 Spain