Bayesian approach for mixture copula model

© Springer Nature Switzerland AG 2019. This paper aims to use the Bayesian estimation as an alternative method for formulating and estimating mixed copula models. This method has claimed to be more efficient than the conventional maximum likelihood estimator as it can deal with the high dimension co...

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Main Authors:	Sukrit Thongkairat, Woraphon Yamaka, Songsak Sriboonchitta
Format:	Book Series
Published:	2019
Subjects:	Computer Science
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065623405&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65524
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Institution:	Chiang Mai University

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spelling	th-cmuir.6653943832-655242019-08-05T04:35:00Z Bayesian approach for mixture copula model Sukrit Thongkairat Woraphon Yamaka Songsak Sriboonchitta Computer Science © Springer Nature Switzerland AG 2019. This paper aims to use the Bayesian estimation as an alternative method for formulating and estimating mixed copula models. This method has claimed to be more efficient than the conventional maximum likelihood estimator as it can deal with the high dimension copula and large parameter estimates under limited sample. In this study, we present various mixed copula functions constructed from both Elliptical and Archimedean copulas. We employ a simulation study to investigate the performance of this estimator for comparison with the maximum likelihood estimator. The results show that the Bayesian estimation is considerably more accurate than maximum likelihood estimator in various scenarios. Finally, we extend the Bayesian mixed copula to the real data and show that our approach perform well in this real data analysis. 2019-08-05T04:35:00Z 2019-08-05T04:35:00Z 2019-01-01 Book Series 1860949X 2-s2.0-85065623405 10.1007/978-3-030-04200-4_58 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065623405&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65524
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science
spellingShingle	Computer Science Sukrit Thongkairat Woraphon Yamaka Songsak Sriboonchitta Bayesian approach for mixture copula model
description	© Springer Nature Switzerland AG 2019. This paper aims to use the Bayesian estimation as an alternative method for formulating and estimating mixed copula models. This method has claimed to be more efficient than the conventional maximum likelihood estimator as it can deal with the high dimension copula and large parameter estimates under limited sample. In this study, we present various mixed copula functions constructed from both Elliptical and Archimedean copulas. We employ a simulation study to investigate the performance of this estimator for comparison with the maximum likelihood estimator. The results show that the Bayesian estimation is considerably more accurate than maximum likelihood estimator in various scenarios. Finally, we extend the Bayesian mixed copula to the real data and show that our approach perform well in this real data analysis.
format	Book Series
author	Sukrit Thongkairat Woraphon Yamaka Songsak Sriboonchitta
author_facet	Sukrit Thongkairat Woraphon Yamaka Songsak Sriboonchitta
author_sort	Sukrit Thongkairat
title	Bayesian approach for mixture copula model
title_short	Bayesian approach for mixture copula model
title_full	Bayesian approach for mixture copula model
title_fullStr	Bayesian approach for mixture copula model
title_full_unstemmed	Bayesian approach for mixture copula model
title_sort	bayesian approach for mixture copula model
publishDate	2019
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065623405&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65524
_version_	1681426284943507456

Bayesian approach for mixture copula model

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