DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Yoojeong | ko |
dc.contributor.author | Choi, K. K. | ko |
dc.contributor.author | Lee, Ikjin | ko |
dc.date.accessioned | 2013-08-22T02:35:02Z | - |
dc.date.available | 2013-08-22T02:35:02Z | - |
dc.date.created | 2013-08-19 | - |
dc.date.created | 2013-08-19 | - |
dc.date.issued | 2010-12 | - |
dc.identifier.citation | STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, v.42, no.6, pp.823 - 833 | - |
dc.identifier.issn | 1615-147X | - |
dc.identifier.uri | http://hdl.handle.net/10203/175642 | - |
dc.description.abstract | The Bayesian method is widely used to identify a joint distribution, which is modeled by marginal distributions and a copula. The joint distribution can be identified by one-step procedure, which directly tests all candidate joint distributions, or by two-step procedure, which first identifies marginal distributions and then copula. The weight-based Bayesian method using two-step procedure and the Markov chain Monte Carlo (MCMC)-based Bayesian method using one-step and two-step procedures were recently developed. In this paper, the one-step weight-based Bayesian method and two-step MCMC-based Bayesian method using the parametric marginal distributions are proposed. Comparison studies among the Bayesian methods have not been thoroughly carried out. In this paper, the weight-based and MCMC-based Bayesian methods using one-step and two-step procedures are compared to see which Bayesian method accurately and efficiently identifies a correct joint distribution through simulation studies. It is validated that the two-step weight-based Bayesian method has the best performance. | - |
dc.language | English | - |
dc.publisher | SPRINGER | - |
dc.subject | SELECTION | - |
dc.subject | RETURNS | - |
dc.subject | COPULAS | - |
dc.title | Comparison study between MCMC-based and weight-based Bayesian methods for identification of joint distribution | - |
dc.type | Article | - |
dc.identifier.wosid | 000283362500002 | - |
dc.identifier.scopusid | 2-s2.0-78049396382 | - |
dc.type.rims | ART | - |
dc.citation.volume | 42 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 823 | - |
dc.citation.endingpage | 833 | - |
dc.citation.publicationname | STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION | - |
dc.identifier.doi | 10.1007/s00158-010-0539-1 | - |
dc.contributor.localauthor | Lee, Ikjin | - |
dc.contributor.nonIdAuthor | Noh, Yoojeong | - |
dc.contributor.nonIdAuthor | Choi, K. K. | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Copula | - |
dc.subject.keywordAuthor | Identification of joint distribution | - |
dc.subject.keywordAuthor | Weight-based Bayesian method | - |
dc.subject.keywordAuthor | MCMC-based Bayesian method | - |
dc.subject.keywordAuthor | One-step procedure | - |
dc.subject.keywordAuthor | Two-step procedure | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | RETURNS | - |
dc.subject.keywordPlus | COPULAS | - |
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