Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54313
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dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorOlga Koshelevaen_US
dc.contributor.authorHung T. Nguyenen_US
dc.date.accessioned2018-09-04T10:11:45Z-
dc.date.available2018-09-04T10:11:45Z-
dc.date.issued2015-12-01en_US
dc.identifier.issn02184885en_US
dc.identifier.other2-s2.0-84954055176en_US
dc.identifier.other10.1142/S0218488515400103en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84954055176&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54313-
dc.description.abstract© 2015 World Scientific Publishing Company. One of the most empirically successful tools for studying dependence between different quantities in econometrics is the tool of vine copulas. In this paper, we explain this empirical success by showing that the most widely used vine copulas are, in effect, the results of using the general fuzzy methodology. To be more precise, vine copulas correspond to a natural extension of the traditional fuzzy methodology, when we allow several different "and"-operations (t-norms), and some of these t-norms can be nonassociative.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleWhy are vine copulas so successful in econometrics?en_US
dc.typeJournalen_US
article.title.sourcetitleInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systemsen_US
article.volume23en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
Appears in Collections:CMUL: Journal Articles

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