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DC Field | Value | Language |
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dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.contributor.author | Olga Kosheleva | en_US |
dc.contributor.author | Hung T. Nguyen | en_US |
dc.date.accessioned | 2018-09-04T10:11:45Z | - |
dc.date.available | 2018-09-04T10:11:45Z | - |
dc.date.issued | 2015-12-01 | en_US |
dc.identifier.issn | 02184885 | en_US |
dc.identifier.other | 2-s2.0-84954055176 | en_US |
dc.identifier.other | 10.1142/S0218488515400103 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84954055176&origin=inward | en_US |
dc.identifier.uri | http://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.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.title | Why are vine copulas so successful in econometrics? | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems | en_US |
article.volume | 23 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
article.stream.affiliations | University of Texas at El Paso | en_US |
article.stream.affiliations | New Mexico State University Las Cruces | en_US |
Appears in Collections: | CMUL: Journal Articles |
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