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...
| Autores: | , , |
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| 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 |
| Sumario: | 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. |
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