Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/51647
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dc.contributor.authorCrane, G.en
dc.contributor.authorVan Der Hoek, J.en
dc.date.issued2008en
dc.identifier.citationAustralian & New Zealand Journal of Statistics, 2008; 50(1):53-67en
dc.identifier.issn1369-1473en
dc.identifier.issn1467-842Xen
dc.identifier.urihttp://hdl.handle.net/2440/51647-
dc.description© 2008 Australian Statistical Publishing Association Inc.en
dc.description.abstractNot only are copula functions joint distribution functions in their own right, they also provide a link between multivariate distributions and their lower-dimensional marginal distributions. Copulas have a structure that allows us to characterize all possible multivariate distributions, and therefore they have the potential to be a very useful statistical tool. Although copulas can be traced back to 1959, there is still much scope for new results, as most of the early work was theoretical rather than practical. We focus on simple practical tools based on conditional expectation, because such tools are not widely available. When dealing with data sets in which the dependence throughout the sample is variable, we suggest that copula-based regression curves may be more accurate predictors of specific outcomes than linear models. We derive simple conditional expectation formulae in terms of copulas and apply them to a combination of simulated and real data.en
dc.description.statementofresponsibilityGlenis J. Crane and John van der Hoeken
dc.language.isoenen
dc.publisherBlackwell Publ Ltden
dc.subjectArchimedean copulas; conditional expectation; Farlie–Gumbel–Morgenstern copulasen
dc.titleConditional expectation formulae for copulasen
dc.typeJournal articleen
dc.identifier.doi10.1111/j.1467-842X.2007.00499.xen
pubs.publication-statusPublisheden
Appears in Collections:Mathematical Sciences publications

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