Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wei, Zheng | - |
dc.contributor.author | Kim, Seongyong | - |
dc.contributor.author | Choi, Boseung | - |
dc.contributor.author | Kim, Daeyoung | - |
dc.date.accessioned | 2021-09-01T22:36:08Z | - |
dc.date.available | 2021-09-01T22:36:08Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.issn | 0219-6220 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/68842 | - |
dc.description.abstract | The exchangeability and radial symmetry assumptions on the dependence structure of the multivariate data are restrictive in practical situations where the variables of interest are not likely to be associated to each other in an identical manner. In this paper, we propose a flexible class of multivariate skew normal copulas to model high-dimensional asymmetric dependence patterns. The proposed copulas have two sets of parameters capturing asymmetric dependence, one for association between the variables and the other for skewness of the variables. In order to efficiently estimate the two sets of parameters, we introduce the block coordinate ascent algorithm and discuss its convergence property. The proposed class of multivariate skew normal copulas is illustrated using a real data set. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WORLD SCIENTIFIC PUBL CO PTE LTD | - |
dc.subject | DIRECTIONAL DEPENDENCE | - |
dc.subject | MODEL | - |
dc.subject | DISTRIBUTIONS | - |
dc.title | Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Boseung | - |
dc.identifier.doi | 10.1142/S021962201750047X | - |
dc.identifier.scopusid | 2-s2.0-85060798965 | - |
dc.identifier.wosid | 000457238100013 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, v.18, no.1, pp.365 - 387 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING | - |
dc.citation.volume | 18 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 365 | - |
dc.citation.endPage | 387 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | DIRECTIONAL DEPENDENCE | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | DISTRIBUTIONS | - |
dc.subject.keywordAuthor | Copula | - |
dc.subject.keywordAuthor | non-exchangeability | - |
dc.subject.keywordAuthor | radial asymmetry | - |
dc.subject.keywordAuthor | skew-normal distribution | - |
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