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Estimation of a Conditional Copula and Association Measures

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Cited by:

  1. Fermanian, Jean-David & Lopez, Olivier, 2018. "Single-index copulas," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 27-55.
  2. Gijbels, Irène & Sznajder, Dominik, 2013. "Testing tail monotonicity by constrained copula estimation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 338-351.
  3. Craiu, V. Radu & Sabeti, Avideh, 2012. "In mixed company: Bayesian inference for bivariate conditional copula models with discrete and continuous outcomes," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 106-120.
  4. Jone Ascorbebeitia & Eva Ferreira & Susan Orbe, 2022. "Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 931-949, December.
  5. Derumigny, Alexis & Fermanian, Jean-David, 2020. "On Kendall’s regression," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  6. Qi Liu & Chun Li & Valentine Wanga & Bryan E. Shepherd, 2018. "Covariate†adjusted Spearman's rank correlation with probability†scale residuals," Biometrics, The International Biometric Society, vol. 74(2), pages 595-605, June.
  7. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
  8. Jean-François Plante, 2017. "Rank correlation under categorical confounding," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
  9. Marra, Giampiero & Radice, Rosalba, 2017. "Bivariate copula additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 99-113.
  10. Wiemann, Paul F.V. & Klein, Nadja & Kneib, Thomas, 2022. "Correcting for sample selection bias in Bayesian distributional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
  11. Michael Stanley Smith, 2021. "Implicit Copulas: An Overview," Papers 2109.04718, arXiv.org.
  12. Grazian, Clara & Dalla Valle, Luciana & Liseo, Brunero, 2022. "Approximate Bayesian conditional copulas," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
  13. Portier, François & Segers, Johan, 2018. "On the weak convergence of the empirical conditional copula under a simplifying assumption," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 160-181.
  14. Irène Gijbels & Marek Omelka & Noël Veraverbeke, 2015. "Estimation of a Copula when a Covariate Affects only Marginal Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1109-1126, December.
  15. Gardes, Laurent & Girard, Stéphane, 2015. "Nonparametric estimation of the conditional tail copula," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 1-16.
  16. Paul Janssen & Luc Duchateau, 2011. "Comments on: Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 271-275, August.
  17. Levi, Evgeny & Craiu, Radu V., 2018. "Bayesian inference for conditional copulas using Gaussian Process single index models," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 115-134.
  18. Zhu, Kailun & Kurowicka, Dorota & Nane, Gabriela F., 2021. "Simplified R-vine based forward regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  19. Jean-David Fermanian & Olivier Lopez, 2015. "Single-index copulae," Working Papers 2015-12, Center for Research in Economics and Statistics.
  20. Gijbels, Irène & Omelka, Marek & Pešta, Michal & Veraverbeke, Noël, 2017. "Score tests for covariate effects in conditional copulas," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 111-133.
  21. Genest Christian & Scherer Matthias, 2023. "When copulas and smoothing met: An interview with Irène Gijbels," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-16, January.
  22. Taoufik Bouezmarni & Félix Camirand Lemyre & Jean-François Quessy, 2019. "On the large-sample behavior of two estimators of the conditional copula under serially dependent data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(7), pages 823-841, October.
  23. Vatter, Thibault & Chavez-Demoulin, Valérie, 2015. "Generalized additive models for conditional dependence structures," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 147-167.
  24. Emura, Takeshi & Lai, Ching-Chieh & Sun, Li-Hsien, 2023. "Change point estimation under a copula-based Markov chain model for binomial time series," Econometrics and Statistics, Elsevier, vol. 28(C), pages 120-137.
  25. Derumigny Alexis & Fermanian Jean-David, 2017. "About tests of the “simplifying” assumption for conditional copulas," Dependence Modeling, De Gruyter, vol. 5(1), pages 154-197, August.
  26. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2020. "The dependence structure between yields and prices: A copula-based model of French farm income," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304313, Agricultural and Applied Economics Association.
  27. Jonas Meier, 2020. "Multivariate Distribution Regression," Diskussionsschriften dp2023, Universitaet Bern, Departement Volkswirtschaft.
  28. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
  29. Asimit, Alexandru V. & Gerrard, Russell & Hou, Yanxi & Peng, Liang, 2016. "Tail dependence measure for examining financial extreme co-movements," Journal of Econometrics, Elsevier, vol. 194(2), pages 330-348.
  30. Jean-François Quessy, 2021. "On nonparametric tests of multivariate meta-ellipticity," Statistical Papers, Springer, vol. 62(5), pages 2283-2310, October.
  31. Ko, Vinnie & Hjort, Nils Lid, 2019. "Model robust inference with two-stage maximum likelihood estimation for copulas," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 362-381.
  32. Bianchi, Pascal & Elgui, Kevin & Portier, François, 2023. "Conditional independence testing via weighted partial copulas," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
  33. Lu Lu & Sujit Ghosh, 2024. "Nonparametric Estimation of Conditional Copula Using Smoothed Checkerboard Bernstein Sieves," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
  34. Neumeyer, Natalie & Omelka, Marek & Hudecová, Šárka, 2019. "A copula approach for dependence modeling in multivariate nonparametric time series," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 139-162.
  35. Benjamin Poignard & Jean-Davis Fermanian, 2014. "Dynamic Asset Correlations Based on Vines," Working Papers 2014-46, Center for Research in Economics and Statistics.
  36. Alexis Derumigny & Jean-David Fermanian, 2018. "About Kendall's regression," Working Papers 2018-01, Center for Research in Economics and Statistics.
  37. Abegaz, Fentaw & Gijbels, Irène & Veraverbeke, Noël, 2012. "Semiparametric estimation of conditional copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 43-73.
  38. Derumigny Alexis & Fermanian Jean-David, 2019. "On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior," Dependence Modeling, De Gruyter, vol. 7(1), pages 292-321, January.
  39. Derumigny, Alexis & Fermanian, Jean-David, 2019. "A classification point-of-view about conditional Kendall’s tau," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 70-94.
  40. Nurudeen A. Adegoke & Andrew Punnett & Marti J. Anderson, 2022. "Estimation of Multivariate Dependence Structures via Constrained Maximum Likelihood," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 240-260, June.
  41. Gijbels Irène & Matterne Margot, 2021. "Study of partial and average conditional Kendall’s tau," Dependence Modeling, De Gruyter, vol. 9(1), pages 82-120, January.
  42. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.
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