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BY 4.0 license Open Access Published by De Gruyter Open Access June 28, 2019

The world of vines

An interview with Claudia Czado

  • Christian Genest EMAIL logo and Matthias Scherer
From the journal Dependence Modeling


[1] Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance Math. Econom. 44(2), 182–198.10.1016/j.insmatheco.2007.02.001Search in Google Scholar

[2] Acar, E.F., C. Genest, and J. Nešlehová (2012). Beyond simplified pair-copula constructions. J. Multivariate Anal. 110, 74–90.10.1016/j.jmva.2012.02.001Search in Google Scholar

[3] Barthel, N., C. Geerdens, C. Czado, and P. Janssen (2019). Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas. Biometrics, to appear. Available at in Google Scholar PubMed

[4] Bauer, A., C. Czado, and T. Klein (2012). Pair-copula constructions for non-Gaussian DAG models. Canad. J. Statist. 40(1), 86–109.10.1002/cjs.10131Search in Google Scholar

[5] Bedford, T. and R.M. Cooke (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Ann. Math. Artif. Intell. 32(1–4), 245–268.10.1023/A:1016725902970Search in Google Scholar

[6] Bedford, T. and R.M. Cooke (2002). Vines – a new graphical model for dependent random variables. Ann. Statist. 30(4), 1031–1068.10.1214/aos/1031689016Search in Google Scholar

[7] Brechmann, E.C. and C. Czado (2013). Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50. Stat. Risk Model. 30(4), 307–342.10.1524/strm.2013.2002Search in Google Scholar

[8] Brechmann, E.C., C. Czado, and K. Aas (2012). Truncated regular vines in high dimensions with application to financial data. Canad. J. Statist. 40(1), 68–85.10.1002/cjs.10141Search in Google Scholar

[9] Czado, C. (1993). Norm restricted maximum likelihood estimators for binary regression models with parametric link. Comm. Statist. Theory Methods 22(8), 2259–2274.10.1080/03610929308831146Search in Google Scholar

[10] Czado, C. (1994). Parametric link modification of both tails in binary regression. Statist. Papers 35(1), 189–201.10.1007/BF02926413Search in Google Scholar

[11] Czado, C. and A. Munk (1998). Assessing the similarity of distributions –finite sample performance of the empirical Mallows distance. J. Statist. Comput. Simulation 60(4), 319–346.10.1080/00949659808811895Search in Google Scholar

[12] Czado, C. and T.J. Santner (1992). The effect of link misspecification on binary regression inference. J. Statist. Plann. Inference 33(2), 213–231.10.1016/0378-3758(92)90069-5Search in Google Scholar

[13] Czado, C. and T.J. Santner (1992). Orthogonalizing parametric link transformation families in binary regression analysis. Canad. J. Statist. 20(1), 51–61.10.2307/3315574Search in Google Scholar

[14] Dißmann, J., E.C. Brechmann, C. Czado, and D. Kurowicka (2013). Selecting and estimating regular vine copulae and application to financial returns. Comput. Statist. Data Anal. 59, 52–69.10.1016/j.csda.2012.08.010Search in Google Scholar

[15] Erhardt, T.M., C. Czado, and U. Schepsmeier (2015). Spatial composite likelihood inference using local C-vines. J. Multivariate Anal. 138, 74–88.10.1016/j.jmva.2015.01.021Search in Google Scholar

[16] Gruber, L.F. and C. Czado (2018). Bayesian model selection of regular vine copulas. Bayesian Anal. 13(4), 1111–1135.10.1214/17-BA1089Search in Google Scholar

[17] Hanea, A.M., D. Kurowicka, and R.M. Cooke (2006). Hybrid method for quantifying and analyzing Bayesian belief nets. Qual. Reliab. Eng. Int. 22(6), 709–729.10.1002/qre.808Search in Google Scholar

[18] Jaworski, P., F. Durante, and W.K. Härdle (2013). Copulae in Mathematical and Quantitative Finance. Springer, Heidelberg.10.1007/978-3-642-35407-6Search in Google Scholar

[19] Joe, H. (2014). Dependence Modeling with Copulas. CRC Press, Boca Raton FL.10.1201/b17116Search in Google Scholar

[20] Killiches, M. and C. Czado (2018). A D-vine copula based model for repeated measurements extending linear mixed models with homogeneous correlation structure. Biometrics 74(3), 997–1005.10.1111/biom.12867Search in Google Scholar PubMed

[21] Killiches, M., D. Kraus, and C. Czado (2017). Examination and visualisation of the simplifying assumption for vine copulas in three dimensions. Aust. N. Z. J. Stat. 59(1), 95–117.10.1111/anzs.12182Search in Google Scholar

[22] Kraus, D. and C. Czado (2017). D-vine copula based quantile regression. Comput. Statist. Data Anal. 110, 1–18.10.1016/j.csda.2016.12.009Search in Google Scholar

[23] Kraus, D. and C. Czado (2017). Growing simplified vine copula trees: improving Dißmann’s algorithm. Available at in Google Scholar

[24] Kreuzer, A. and C. Czado (2019). E˛cient Bayesian inference for univariate and multivariate non linear state space models with univariate autoregressive state equation. Available at in Google Scholar

[25] Kurowicka, D. and R.M. Cooke (2006). Uncertainty Analysis with High Dimensional Dependence Modelling. John Wiley & Sons, Chichester.10.1002/0470863072Search in Google Scholar

[26] Kurowicka, D. and R.M. Cooke (2007). Sampling algorithms for generating joint uniform distributions using the vine-copula method. Comput. Statist. Data Anal. 51(6), 2889–2906.10.1016/j.csda.2006.11.043Search in Google Scholar

[27] Kurowicka, D. and H. Joe (2010). Dependence Modeling: Vine Copula Handbook. World Scientific Publishing, Singapore.10.1142/7699Search in Google Scholar

[28] Kurz, M.S. (2017). pacotest: Testing for Partial Copulas and the Simplifying Assumption in Vine Copulas. R package version 0.3.1. Available on CRAN.Search in Google Scholar

[29] Kurz, M.S. and F. Spanhel (2017). Testing the simplifying assumption in high-dimensional vine copulas. Available at in Google Scholar

[30] Min, A. and C. Czado (2010). Bayesian inference for multivariate copulas using pair-copula constructions. J. Financial Econom. 8(4), 511–546.10.1093/jjfinec/nbp031Search in Google Scholar

[31] Morales-Nápoles, O. (2010). Counting vines. In D. Kurowicka and H. Joe (Eds.), Dependence Modeling: Vine Copula Handbook, pp. 189–218. World Scientific Publishing, Singapore.10.1142/9789814299886_0009Search in Google Scholar

[32] Müller, D. and C. Czado (2018). Representing sparse Gaussian DAGs as sparse R-vines allowing for non-Gaussian dependence. J. Comput. Graph. Statist. 27(2), 334–344.10.1080/10618600.2017.1366911Search in Google Scholar

[33] Müller, D. and C. Czado (2019). Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso. Comput. Statist. Data Anal. 137, 211–232.10.1016/j.csda.2019.02.007Search in Google Scholar

[34] Munk, A. and C. Czado (1998). Nonparametric validation of similar distributions and assessment of goodness of fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 60(1), 223–241.10.1111/1467-9868.00121Search in Google Scholar

[35] Nagler, T. (2017). kdevine: Multivariate Kernel Density Estimation with Vine Copulas. R package version 0.4.2. Available on CRAN.Search in Google Scholar

[36] Nagler, T. and C. Czado (2016). Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas. J. Multivariate Anal. 151, 69–89.10.1016/j.jmva.2016.07.003Search in Google Scholar

[37] Nagler, T. and D. Kraus (2017). vinereg: D-Vine Quantile Regression. R package version 0.2.0.Search in Google Scholar

[38] Nagler, T., C. Schellhase, and C. Czado (2017). Nonparametric estimation of simplified vine copula models: comparison of methods. Depend. Model. 5, 99–120.10.1515/demo-2017-0007Search in Google Scholar

[39] Newton, M.A., C. Czado, and R. Chappell (1996). Bayesian inference for semiparametric binary regression. J. Amer. Statist. Assoc. 91(433), 142–153.10.1080/01621459.1996.10476671Search in Google Scholar

[40] Panagiotelis, A., C. Czado, and H. Joe (2012). Pair copula constructions for multivariate discrete data. J. Amer. Statist. Assoc. 107(499), 1063–1072.10.1080/01621459.2012.682850Search in Google Scholar

[41] Schallhorn, N., D. Kraus, T. Nagler, and C. Czado (2017). D-vine quantile regression with discrete variables. Available at in Google Scholar

[42] Schellhase, C. (2017). pencopulaCond: Estimating Non-Simplified Vine Copulas Using Penalized Splines. R package version 0.2. Available on CRAN.Search in Google Scholar

[43] Schellhase, C. (2017). penRvine: Flexible R-Vines Estimation Using Bivariate Penalized Splines. R package version 0.2. Available on CRAN.Search in Google Scholar

[44] Schellhase, C. and F. Spanhel (2018). Estimating non-simplified vine copulas using penalized splines. Stat. Comput. 28(2), 387–409.10.1007/s11222-017-9737-7Search in Google Scholar

[45] Schepsmeier, U., J. Stöber, E.C. Brechmann, B. Gräler, T. Nagler, and T. Erhardt et al. (2018). VineCopula: Statistical Inference of Vine Copulas. R package version 2.1.8. Available on CRAN.Search in Google Scholar

[46] Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8, 229–231.Search in Google Scholar

[47] Stöber, J. and C. Czado (2014). Regime switches in the dependence structure of multidimensional financial data. Comput. Statist. Data Anal. 76, 672–686.10.1016/j.csda.2013.04.002Search in Google Scholar

[48] Stöber, J., H.G. Hong, C. Czado, and P. Ghosh (2015). Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses. Comput. Statist. Data Anal. 88, 28–39.10.1016/j.csda.2015.02.001Search in Google Scholar

[49] Taqqu, M.S. and C. Czado (1985). A survey of functional laws of the iterated logarithm for self-similar processes. Comm. Statist. Stochastic Models 1(1), 77–115.10.1080/15326348508807005Search in Google Scholar

[50] Vatter, T. and T. Nagler (2018). Generalized additive models for pair-copula constructions. J. Comput. Graph. Statist. 27(4), 715–727.10.1080/10618600.2018.1451338Search in Google Scholar

Received: 2019-05-21
Accepted: 2019-05-28
Published Online: 2019-06-28

© 2019 Christian Genest et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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