A Mixture of Regular Vines for Multiple Dependencies

To uncover complex hidden dependency structures among variables, researchers have used a mixture of vine copula constructions. To date, these have been limited to a subclass of regular vine models, the so-called drawable vine, fitting only one type of bivariate copula for all variable pairs. However...

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Main Author: Fadhah Amer Alanazi
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2021/5559518
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author Fadhah Amer Alanazi
author_facet Fadhah Amer Alanazi
author_sort Fadhah Amer Alanazi
collection DOAJ
description To uncover complex hidden dependency structures among variables, researchers have used a mixture of vine copula constructions. To date, these have been limited to a subclass of regular vine models, the so-called drawable vine, fitting only one type of bivariate copula for all variable pairs. However, the variation of complex hidden correlations from one pair of variables to another is more likely to be present in many real datasets. Single-type bivariate copulas are unable to deal with such a problem. In addition, the regular vine copula model is much more capable and flexible than its subclasses. Hence, to fully uncover and describe complex hidden dependency structures among variables and provide even further flexibility to the mixture of regular vine models, a mixture of regular vine models, with a mixed choice of bivariate copulas, is proposed in this paper. The model was applied to simulated and real data to illustrate its performance. The proposed model shows significant performance over the mixture of R-vine densities with a single copula family fitted to all pairs.
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institution Kabale University
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spelling doaj-art-58d590bd20984cad9948da8e3cd4c18e2025-02-03T06:43:49ZengWileyJournal of Probability and Statistics1687-952X1687-95382021-01-01202110.1155/2021/55595185559518A Mixture of Regular Vines for Multiple DependenciesFadhah Amer Alanazi0General Science Department, Prince Sultan University, Riyadh, Saudi ArabiaTo uncover complex hidden dependency structures among variables, researchers have used a mixture of vine copula constructions. To date, these have been limited to a subclass of regular vine models, the so-called drawable vine, fitting only one type of bivariate copula for all variable pairs. However, the variation of complex hidden correlations from one pair of variables to another is more likely to be present in many real datasets. Single-type bivariate copulas are unable to deal with such a problem. In addition, the regular vine copula model is much more capable and flexible than its subclasses. Hence, to fully uncover and describe complex hidden dependency structures among variables and provide even further flexibility to the mixture of regular vine models, a mixture of regular vine models, with a mixed choice of bivariate copulas, is proposed in this paper. The model was applied to simulated and real data to illustrate its performance. The proposed model shows significant performance over the mixture of R-vine densities with a single copula family fitted to all pairs.http://dx.doi.org/10.1155/2021/5559518
spellingShingle Fadhah Amer Alanazi
A Mixture of Regular Vines for Multiple Dependencies
Journal of Probability and Statistics
title A Mixture of Regular Vines for Multiple Dependencies
title_full A Mixture of Regular Vines for Multiple Dependencies
title_fullStr A Mixture of Regular Vines for Multiple Dependencies
title_full_unstemmed A Mixture of Regular Vines for Multiple Dependencies
title_short A Mixture of Regular Vines for Multiple Dependencies
title_sort mixture of regular vines for multiple dependencies
url http://dx.doi.org/10.1155/2021/5559518
work_keys_str_mv AT fadhahameralanazi amixtureofregularvinesformultipledependencies
AT fadhahameralanazi mixtureofregularvinesformultipledependencies