Some approximations of n-copulas
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DOI: 10.1007/s00184-009-0259-y
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Cited by:
- Ansari Jonathan & Rüschendorf Ludger, 2021. "Sklar’s theorem, copula products, and ordering results in factor models," Dependence Modeling, De Gruyter, vol. 9(1), pages 267-306, January.
- Ansari Jonathan & Rüschendorf Ludger, 2018. "Ordering risk bounds in factor models," Dependence Modeling, De Gruyter, vol. 6(1), pages 259-287, November.
- Fabrizio Durante & Juan Fernández Sánchez & Wolfgang Trutschnig, 2020. "Spatially homogeneous copulas," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 607-626, April.
- Durante, Fabrizio & Sánchez, Juan Fernández, 2012. "On the approximation of copulas via shuffles of Min," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1761-1767.
- Griessenberger Florian & Trutschnig Wolfgang, 2022. "Maximal asymmetry of bivariate copulas and consequences to measures of dependence," Dependence Modeling, De Gruyter, vol. 10(1), pages 245-269, January.
- Chamnan Wongtawan & Sumetkijakan Songkiat, 2023. "Characterization of pre-idempotent Copulas," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-12, January.
- Coblenz, Maximilian & Grothe, Oliver & Schreyer, Manuela & Trutschnig, Wolfgang, 2018. "On the length of copula level curves," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 347-365.
- Durante Fabrizio & Fernández-Sánchez Juan & Trutschnig Wolfgang, 2014. "Solution to an open problem about a transformation on the space of copulas," Dependence Modeling, De Gruyter, vol. 2(1), pages 1-8, November.
- Harder, Michael & Stadtmüller, Ulrich, 2014. "Maximal non-exchangeability in dimension d," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 31-41.
- Durante, Fabrizio & Fernández Sánchez, Juan & Trutschnig, Wolfgang, 2014. "Multivariate copulas with hairpin support," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 323-334.
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Keywords
Copula; Checkerboard approximation; Bernstein approximation; Shuffle-of-min;All these keywords.
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