References
- K. Aas, Pair-copula constructions for financial applications: A review, Econometrics 4(4) (2016), pp. 1–15. doi: https://doi.org/10.3390/econometrics4040043
- K. Aas, C. Czado, A. Frigessi, and H. Bakken, Pair-copula constructions of multiple dependence, Insurance: Math. Econom. 44(2) (2009), pp. 182–198.
- C. Almeida and C. Czado, Efficient Bayesian inference for stochastic time-varying copula models, Comput. Stat. Data Anal. 56(6) (2012), pp. 1511–1527. doi: https://doi.org/10.1016/j.csda.2011.08.015
- C. Almeida, C. Czado, and H. Manner, Modeling high-dimensional time-varying dependence using dynamic D-vine models, Appl. Stoch. Models. Bus. Ind. 32(5) (2016), pp. 621–638. doi: https://doi.org/10.1002/asmb.2182
- V. Arakelian and D. Karlis, Clustering dependencies via mixtures of copulas, Commun. Stat. - Simul. Comput. 43(7) (2013), pp. 1644–1661. doi: https://doi.org/10.1080/03610918.2012.752832
- T. Bedford and R. Cooke, Probability density decomposition for conditionally dependent random variables modeled by vines, Ann. Math. Artif. Intell. 32 (2001), pp. 245–268. doi: https://doi.org/10.1023/A:1016725902970
- T. Bedford and R.M. Cooke, Vines: A new graphical model for dependent random variables, Ann. Stat. 30(4) (2002), pp. 1031–1068. doi: https://doi.org/10.1214/aos/1031689016
- E.K. Brechmann and U. Schepsmeier, Modeling dependence with C- and D-Vine copulas: The R package CDVine, J. Stat. Softw. 52(3) (2013), pp. 1–27. doi: https://doi.org/10.18637/jss.v052.i03
- E. Cuvelier and N.M. Fraiture, Clayton copula and mixture decomposition, in Applied Stochastic Models and Data Analysis (ASMDA), J. Janssen and P. Lenca, eds., Brest, France, 2005, pp. 699–708.
- C. Czado, Analyzing Dependent Data with Vine Copulas: A Practical Guide With R, Springer International Publishing, Springer Nature Switzerland AG, 2019.
- J. DisMann, E.C. Brechmann, C. Czado, and D. Kurowicka, Selecting and estimating regular vine copulae and application to financial returns, Comput. Stat. Data Anal. 59(C) (2013), pp. 52–69. doi: https://doi.org/10.1016/j.csda.2012.08.010
- V. Erhardt and C. Czado, Modeling dependent yearly claim totals including zero claims in private health insurance, Scand. Actuar. J. 2 (2012), pp. 106–129. doi: https://doi.org/10.1080/03461238.2010.489762
- T.M. Erhardt and C. Czado, Standardized drought indices: A novel univariate and multivariate approach, J. Royal Stat. Soc.: Ser. C (Appl. Stat.) 67(3) (2018), pp. 643–664.
- O. Evkaya, Mixture of vines for dependence modeling: Finite mixture and CD-vine approaches with applications, Ph.D. thesis, Middle East Technical University, Ankara, 2018.
- M. Frechet, Sur les tableaux de corrélation dont les marges sont données, Annales de I'Université De Lyon, Section A, Sciences Mathematiques et Astronomic (3)14 (1951), pp. 53–77.
- A. Ghalanos, rugarch: Univariate GARCH models, R package version 1.3-8, 2017.
- L. Hu, Dependence patterns across financial markets: a mixed copula approach, Appl. Financ. Econom. 16(10) (2006), pp. 717–729. doi: https://doi.org/10.1080/09603100500426515
- H. Joe, Families of m-variate distributions with given margins and m(m−1)/2 bivariate dependence parameters, in IMS Lecture Notes-Monograph Ser. Hayward, 1996, pp. 120–141.
- H. Joe, Dependence Modeling with Copulas, Chapman and Hall/CRC, New York, 2014.
- D. Kim, J.-M. Kim, S.-M. Liao, and Y.-S. Jung, Mixture of D-vine copulas for modeling dependence, Comput. Stat. Data Anal. 64 (2013), pp. 1–19. doi: https://doi.org/10.1016/j.csda.2013.02.018
- I. Kosmidis and D. Karlis, Model-based clustering using copulas with applications, Stat. Comput. 26 (2016), pp. 1079–1099. doi: https://doi.org/10.1007/s11222-015-9590-5
- D. Kurowicka and R.M. Cooke, Uncertainty Analysis With High Dimensional Dependence Modelling, Wiley Series in Probability and Statistics, JohnWiley & Sons Ltd, Chichester, 2006.
- D. Kurowicka and H. Joe, Dependence Modeling: Handbook on Vine Copulae, World Scientific Publishing, Singapore/SG, 2010.
- E.D. Mathieu Vrac and Alain Chédin, Clustering a global field of atmospheric profiles by mixture decomposition of copulas, J. Atmospheric Oceanic Technol. 22 (2005), pp. 1445–1459. doi: https://doi.org/10.1175/JTECH1795.1
- R.D. Matteis, Fitting copulas to data, Ph.D. thesis, Swiss Federal Institute of Technology Zurich, 2001.
- K. Mullen, D. Ardia, D. Gil, D. Windover, and J. Cline, DEoptim: An R package for global optimization by differential evolution, J. Stat. Softw. 40 (2011), pp. 1–26. doi: https://doi.org/10.18637/jss.v040.i06
- R. Core Team and R. Foundation for Statistical Computing, R: A language and environment for statistical computing, 2017; software available at http://www.R-project.org/.
- M. Sklar, Fonctions de Répartition À N Dimensions Et Leurs Marges, Université Paris, 8 1959.
- M. Sun, I. Konstantelos, and G. Strbac, C-vine copula mixture model for clustering of residential electrical load pattern data, IEEE Trans. Power Syst. 32(3) (2017), pp. 2382–2393. doi: https://doi.org/10.1109/TPWRS.2016.2614366