Tree-based conditional copula estimation
This article proposes a regression tree procedure to estimate conditional copulas. The associated algorithm determines classes of observations based on covariate values and fits a simple parametric copula model on each class. The association parameter changes from one class to another, allowing for...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-02-01
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| Series: | Dependence Modeling |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/demo-2024-0010 |
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| _version_ | 1850198003319570432 |
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| author | Bonacina Francesco Lopez Olivier Thomas Maud |
| author_facet | Bonacina Francesco Lopez Olivier Thomas Maud |
| author_sort | Bonacina Francesco |
| collection | DOAJ |
| description | This article proposes a regression tree procedure to estimate conditional copulas. The associated algorithm determines classes of observations based on covariate values and fits a simple parametric copula model on each class. The association parameter changes from one class to another, allowing for non-linearity in the dependence structure modeling. It also allows the definition of classes of observations on which the so-called “simplifying assumption” holds reasonably well. When considering observations belonging to a given class separately, the association parameter no longer depends on the covariates according to our model. In this article, we derive asymptotic consistency results for the regression tree procedure and show that the proposed pruning methodology, i.e., the model selection techniques selecting the appropriate number of classes, is optimal in some sense. Simulations provide finite sample results, and an analysis of data of cases of human influenza presents the practical behavior of the procedure. |
| format | Article |
| id | doaj-art-be5a494b095348aebc28529a13da4b8b |
| institution | OA Journals |
| issn | 2300-2298 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Dependence Modeling |
| spelling | doaj-art-be5a494b095348aebc28529a13da4b8b2025-08-20T02:12:59ZengDe GruyterDependence Modeling2300-22982025-02-01131437310.1515/demo-2024-0010Tree-based conditional copula estimationBonacina Francesco0Lopez Olivier1Thomas Maud2Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, LPSM, 4 place Jussieu, F-75005 Paris, FranceCREST Laboratory, CNRS, Groupe des Écoles Nationales d’Économie et Statistique, Ecole Polytechnique, Institut Polytechnique de Paris, 5 avenue Henry Le Chatelier91120 Palaiseau, FranceSorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, LPSM, 4 place Jussieu, F-75005 Paris, FranceThis article proposes a regression tree procedure to estimate conditional copulas. The associated algorithm determines classes of observations based on covariate values and fits a simple parametric copula model on each class. The association parameter changes from one class to another, allowing for non-linearity in the dependence structure modeling. It also allows the definition of classes of observations on which the so-called “simplifying assumption” holds reasonably well. When considering observations belonging to a given class separately, the association parameter no longer depends on the covariates according to our model. In this article, we derive asymptotic consistency results for the regression tree procedure and show that the proposed pruning methodology, i.e., the model selection techniques selecting the appropriate number of classes, is optimal in some sense. Simulations provide finite sample results, and an analysis of data of cases of human influenza presents the practical behavior of the procedure.https://doi.org/10.1515/demo-2024-0010conditional copularegression treesasymptotic theory62g0862g2062h3062p10 |
| spellingShingle | Bonacina Francesco Lopez Olivier Thomas Maud Tree-based conditional copula estimation Dependence Modeling conditional copula regression trees asymptotic theory 62g08 62g20 62h30 62p10 |
| title | Tree-based conditional copula estimation |
| title_full | Tree-based conditional copula estimation |
| title_fullStr | Tree-based conditional copula estimation |
| title_full_unstemmed | Tree-based conditional copula estimation |
| title_short | Tree-based conditional copula estimation |
| title_sort | tree based conditional copula estimation |
| topic | conditional copula regression trees asymptotic theory 62g08 62g20 62h30 62p10 |
| url | https://doi.org/10.1515/demo-2024-0010 |
| work_keys_str_mv | AT bonacinafrancesco treebasedconditionalcopulaestimation AT lopezolivier treebasedconditionalcopulaestimation AT thomasmaud treebasedconditionalcopulaestimation |