Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists

Regular vine copulas (R-vines) provide a comprehensive framework for modeling high- dimensional dependencies using a hierarchy of trees and conditional pair-copulas. While the graphical structure of R-vines is traditionally derived from data, this work introduces a novel approach by utilizing a (con...

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Detalles Bibliográficos
Autores: Carrera, D., Santana, R., Lozano, J.A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/2044
Acceso en línea:http://hdl.handle.net/20.500.11824/2044
Access Level:acceso abierto
Palabra clave:copula
dependence list
genetic algorithm
optimization
regular vine
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spelling Learning the Graph Structure of Regular Vine-Copulas from Dependence ListsCarrera, D.Santana, R.Lozano, J.A.copuladependence listgenetic algorithmoptimizationregular vineRegular vine copulas (R-vines) provide a comprehensive framework for modeling high- dimensional dependencies using a hierarchy of trees and conditional pair-copulas. While the graphical structure of R-vines is traditionally derived from data, this work introduces a novel approach by utilizing a (conditional) pairwise dependence list. Our primary goal is to construct R-vine graphs that include the maximum possible number of dependence relationships specied in such lists. To tackle this optimization challenge, characterized by exponential growth in the search space and the structural constraints of R-vines, we propose two distinct methodologies: A 0-1 linear programming formulation and a Genetic Algorithm (GA). Additionally, the Randomized Constructive Technique (RCT) is employed to generate initial population of the GA, serving as a baseline for our comparison. Experimental results reveal the superior performance of the GA over the RCT in terms of success rate, incorporating more relationships than RCT into the constructed R-vine graphs and achieving near- optimal or optimal graph structures.IT1504-22 PID2022-137442NB-I00 PID2023-149195NB-I00202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/2044reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001142-Sinfo:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/20442026-06-19T12:47:47Z
dc.title.none.fl_str_mv Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
title Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
spellingShingle Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
Carrera, D.
copula
dependence list
genetic algorithm
optimization
regular vine
title_short Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
title_full Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
title_fullStr Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
title_full_unstemmed Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
title_sort Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists
dc.creator.none.fl_str_mv Carrera, D.
Santana, R.
Lozano, J.A.
author Carrera, D.
author_facet Carrera, D.
Santana, R.
Lozano, J.A.
author_role author
author2 Santana, R.
Lozano, J.A.
author2_role author
author
dc.subject.none.fl_str_mv copula
dependence list
genetic algorithm
optimization
regular vine
topic copula
dependence list
genetic algorithm
optimization
regular vine
description Regular vine copulas (R-vines) provide a comprehensive framework for modeling high- dimensional dependencies using a hierarchy of trees and conditional pair-copulas. While the graphical structure of R-vines is traditionally derived from data, this work introduces a novel approach by utilizing a (conditional) pairwise dependence list. Our primary goal is to construct R-vine graphs that include the maximum possible number of dependence relationships specied in such lists. To tackle this optimization challenge, characterized by exponential growth in the search space and the structural constraints of R-vines, we propose two distinct methodologies: A 0-1 linear programming formulation and a Genetic Algorithm (GA). Additionally, the Randomized Constructive Technique (RCT) is employed to generate initial population of the GA, serving as a baseline for our comparison. Experimental results reveal the superior performance of the GA over the RCT in terms of success rate, incorporating more relationships than RCT into the constructed R-vine graphs and achieving near- optimal or optimal graph structures.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/2044
url http://hdl.handle.net/20.500.11824/2044
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001142-S
info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
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