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Nonparametric estimation of multivariate extreme-value copulas

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  • Gudendorf, Gordon
  • Segers, Johan

Abstract

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Suggested Citation

  • Gudendorf, Gordon & Segers, Johan, 2012. "Nonparametric estimation of multivariate extreme-value copulas," LIDAM Reprints ISBA 2012011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2012011
    Note: In : Journal of Statistical Planning and Inference, vol. 142, no.12, p. 3073-3085 (2012)
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    Citations

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    Cited by:

    1. Berghaus, Betina & Segers, Johan, 2018. "Weak convergence of the weighted empirical beta copula process," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 266-281.
    2. Gery Geenens & Arthur Charpentier & Davy Paindaveine, 2014. "Probit Transformation for Nonparametric Kernel Estimation of the Copula Density," Working Papers ECARES ECARES 2014-23, ULB -- Universite Libre de Bruxelles.
    3. Mhalla, Linda & Chavez-Demoulin, Valérie & Naveau, Philippe, 2017. "Non-linear models for extremal dependence," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 49-66.
    4. Marcon, Giulia & Padoan, Simone & Naveau, Philippe & Muliere, Pietro & Segers, Johan, 2016. "Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials," LIDAM Discussion Papers ISBA 2016020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Zou, Nan, 2023. "Reweighted madogram-type estimator of Pickands dependence function," Statistics & Probability Letters, Elsevier, vol. 195(C).
    6. Bucher, Axel & Segers, Johan, 2013. "Extreme value copula estimation based on block maxima of a multivariate stationary time series," LIDAM Discussion Papers ISBA 2013049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Bucher, Axel & Segers, Johan & Volgushev, Stanislav, 2013. "When uniform weak convergence fails: empirical processes for dependence functions via epi- and hypographs," LIDAM Discussion Papers ISBA 2013019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Boulin, Alexis & Di Bernardino, Elena & Laloë, Thomas & Toulemonde, Gwladys, 2022. "Non-parametric estimator of a multivariate madogram for missing-data and extreme value framework," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    9. Enkelejd Hashorva & Simone A. Padoan & Stefano Rizzelli, 2021. "Multivariate extremes over a random number of observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 845-880, September.
    10. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    11. Grothe, Oliver & Schnieders, Julius & Segers, Johan, 2013. "Measuring Association and Dependence Between Random Vectors," LIDAM Discussion Papers ISBA 2013026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Kojadinovic, Ivan & Stemikovskaya, Kristina, 2019. "Subsampling (weighted smooth) empirical copula processes," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 704-723.
    13. Zofia Gródek-Szostak & Gabriela Malik & Danuta Kajrunajtys & Anna Szeląg-Sikora & Jakub Sikora & Maciej Kuboń & Marcin Niemiec & Joanna Kapusta-Duch, 2019. "Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function," Sustainability, MDPI, vol. 11(15), pages 1-14, August.
    14. Gardes, Laurent & Girard, Stéphane, 2015. "Nonparametric estimation of the conditional tail copula," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 1-16.
    15. Grothe, Oliver & Schnieders, Julius & Segers, Johan, 2014. "Measuring association and dependence between random vectors," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 96-110.
    16. Yongzhao Chen & Ka Chun Cheung & Sheung Chi Phillip Yam & Fei Lung Yuen & Jia Zeng, 2023. "On the Diversification Effect in Solvency II for Extremely Dependent Risks," Risks, MDPI, vol. 11(8), pages 1-22, August.

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