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Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas

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Cited by:

  1. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
  2. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
  3. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
  4. Ash Abebe & Huybrechts F. Bindele & Masego Otlaadisa & Boikanyo Makubate, 2021. "Robust estimation of single index models with responses missing at random," Statistical Papers, Springer, vol. 62(5), pages 2195-2225, October.
  5. Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
  6. Panos Fousekis & Dimitra Tzaferi, 2022. "Tail price risk spillovers along the US beef and pork supply chains," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(2), pages 383-399, April.
  7. Laverny, Oskar & Masiello, Esterina & Maume-Deschamps, Véronique & Rullière, Didier, 2021. "Dependence structure estimation using Copula Recursive Trees," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
  8. Matthias Killiches & Claudia Czado, 2018. "A D‐vine copula‐based model for repeated measurements extending linear mixed models with homogeneous correlation structure," Biometrics, The International Biometric Society, vol. 74(3), pages 997-1005, September.
  9. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  10. Rémillard, Bruno & Nasri, Bouchra & Bouezmarni, Taoufik, 2017. "On copula-based conditional quantile estimators," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 14-20.
  11. Han, Xuyuan & Liu, Zhenya & Wang, Shixuan, 2022. "An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting," Journal of Commodity Markets, Elsevier, vol. 25(C).
  12. Christian Schellhase & Torben Kuhlenkasper, 2017. "Semi-parametric estimation of income mobility with D‑vines using bivariate penalised splines," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(2), pages 107-134, October.
  13. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
  14. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.
  15. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
  16. Haarstad, Aleksander H. & Lavrutich, Maria & Strypet, Kristian & Strøm, Eivind, 2022. "Multi-commodity price risk hedging in the Atlantic salmon farming industry," Journal of Commodity Markets, Elsevier, vol. 25(C).
  17. Nagler, Thomas, 2018. "A generic approach to nonparametric function estimation with mixed data," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 326-330.
  18. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
  19. Kim, Wongon & Yoon, Heonjun & Lee, Guesuk & Kim, Taejin & Youn, Byeng D., 2020. "A new calibration metric that considers statistical correlation: Marginal Probability and Correlation Residuals," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  20. Grazian, Clara & Dalla Valle, Luciana & Liseo, Brunero, 2022. "Approximate Bayesian conditional copulas," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
  21. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  22. Maximilian Coblenz & Simon Holz & Hans‐Jörg Bauer & Oliver Grothe & Rainer Koch, 2020. "Modelling fuel injector spray characteristics in jet engines by using vine copulas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 863-886, August.
  23. Gildas Mazo & François Portier, 2021. "Parametric versus nonparametric: The fitness coefficient," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1344-1383, December.
  24. Genest Christian & Scherer Matthias, 2019. "The world of vines: An interview with Claudia Czado," Dependence Modeling, De Gruyter, vol. 7(1), pages 169-180, January.
  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. Nagler Thomas & Czado Claudia & Schellhase Christian, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
  27. Lu Lu & Sujit Ghosh, 2024. "Nonparametric Estimation of Conditional Copula Using Smoothed Checkerboard Bernstein Sieves," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
  28. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
  29. Aas Kjersti & Nagler Thomas & Jullum Martin & Løland Anders, 2021. "Explaining predictive models using Shapley values and non-parametric vine copulas," Dependence Modeling, De Gruyter, vol. 9(1), pages 62-81, January.
  30. Acar, Elif F. & Czado, Claudia & Lysy, Martin, 2019. "Flexible dynamic vine copula models for multivariate time series data," Econometrics and Statistics, Elsevier, vol. 12(C), pages 181-197.
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