Keywords: copula; conditional independences; Regular-vine; truncated vine; cherry-tree copula
@article{10_14736_kyb_2017_3_0437,
author = {Kov\'acs, Edith and Sz\'antai, Tam\'as},
title = {On the connection between cherry-tree copulas and truncated {R-vine} copulas},
journal = {Kybernetika},
pages = {437--460},
year = {2017},
volume = {53},
number = {3},
doi = {10.14736/kyb-2017-3-0437},
mrnumber = {3684679},
zbl = {06819617},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-3-0437/}
}
TY - JOUR AU - Kovács, Edith AU - Szántai, Tamás TI - On the connection between cherry-tree copulas and truncated R-vine copulas JO - Kybernetika PY - 2017 SP - 437 EP - 460 VL - 53 IS - 3 UR - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-3-0437/ DO - 10.14736/kyb-2017-3-0437 LA - en ID - 10_14736_kyb_2017_3_0437 ER -
%0 Journal Article %A Kovács, Edith %A Szántai, Tamás %T On the connection between cherry-tree copulas and truncated R-vine copulas %J Kybernetika %D 2017 %P 437-460 %V 53 %N 3 %U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-3-0437/ %R 10.14736/kyb-2017-3-0437 %G en %F 10_14736_kyb_2017_3_0437
Kovács, Edith; Szántai, Tamás. On the connection between cherry-tree copulas and truncated R-vine copulas. Kybernetika, Tome 53 (2017) no. 3, pp. 437-460. doi: 10.14736/kyb-2017-3-0437
[1] Aas, K., Czado, C., Frigessi, A., Bakken, H.: Pair-copula constructions of multiple dependence. Insur. Math. Econom. 44 (2009), 182-198. | DOI | MR
[2] Acar, E. F., Genest, C., Nešlehová, J.: Beyond simplified pair-copula constructions. J. Multivariate Anal. 110 (2012), 74-90. | DOI | MR
[3] Bauer, A., Czado, C., Klein, T.: Pair-copula construction for non-Gaussian DAG models. Canad. J. Stat. 40 (2012), 1, 86-109. | DOI | MR
[4] Bedford, T., Cooke, R.: Probability density decomposition for conditionally dependent random variables modeled by vines. Ann. Math. Artif. Intell. 32 (2001), 245-268. | DOI | MR
[5] Bedford, T., Cooke, R.: Vines - a new graphical model for dependent random variables. Ann. Statist. 30 (2002), 4, 1031-1068. | DOI | MR
[6] Brechmann, E. C., Czado, C., Aas, K.: Truncated regular vines in high dimensions with applications to financial data. Canad. J. Statist. 40 (2012), 1, 68-85. | DOI | MR
[7] Bukszár, J., Prékopa, A.: Probability bounds with cherry trees. Math. Oper. Res. 26 (2001), 174-192. | DOI | MR
[8] Bukszár, J., Szántai, T.: Probability bounds given by hypercherry trees. Optim. Methods Software 17 (2002), 409-422. | DOI | MR
[9] Cover, T. M., Thomas, J. A.: Elements of Information Theory. Wiley Interscience, New York 1991. | DOI | MR
[10] Czado, C.: Pair-copula constructions of multivariate copulas. In: Copula Theory and Its Applications (P. Jaworski, F. Durante, W. Härdle, and T. Rychlik, eds.), Springer, Berlin 2010. | DOI | MR
[11] Dissman, J., Brechmann, E. C., Czado, C., Kurowicka, D.: Selecting and estimating regular vine copulae and application to financial returns. Comput. Statist. Data Anal. 59 (2013), 52-69. | DOI | MR
[12] Hanea, A., Kurowicka, D., Cooke, R.: Hybrid method for quantifying and analyzing Bayesian belief networks. Qual. Reliab. Engrg. 22 (2006), 708-729. | DOI
[13] Haff, I. Hobaek, Aas, K., Frigessi, A.: On the simplified pair-copula construction - simply useful or too simplistic?. J. Multivariate Anal. 101 (2010), 5, 1296-1310. | DOI | MR
[14] Haff, I. Hobaek, Segers, J.: Nonparametric estimation of pair-copula constructions with the empirical pair-copula. 2010. | arXiv
[15] Hobaek-Haff, I., Aas, K., Frigessi, A., Lacal, V.: Structure learning in Bayesian Networks using regular vines. Computat. Statist. Data Anal. 101 (2016), 186-208. | DOI | MR
[16] Joe, H.: Multivariate Models and Dependence Concepts. Chapman and Hall, London 1997. | DOI | MR | Zbl
[17] Kovács, E., Szántai, T.: On the approximation of discrete multivariate probability distribution using the new concept of $t$-cherry junction tree. Lect. Notes Economics Math. Systems 633, Proc. IFIP/IIASA/GAMM Workshop on Coping with Uncertainty, Robust Solutions, 2008, IIASA, Laxenburg 2010, pp. 39-56. | DOI | MR
[18] Kovács, E., Szántai, T.: Multivariate copula expressed by lower dimensional copulas. 2010. | arXiv
[19] Kovács, E., Szántai, T.: Hypergraphs in the characterization of regular-vine copula structures. In: Proc. 13th International Conference on Mathematics and its Applications, Timisoara 2012(a), pp. 335-344.
[20] Kovács, E., Szántai, T.: Vine copulas as a mean for the construction of high dimensional probability distribution associated to a Markov network. 2012(b). | arXiv
[21] Kurowicka, D., Cooke, R.: The vine copula method for representing high dimensional dependent distributions: Application to continuous belief nets. In: Proc. 2002 Winter Simulation Conference 2002, pp. 270-278. | DOI
[22] Kurowicka, D., Cooke, R. M.: Uncertainty Analysis with High Dimensional Dependence Modelling. John Wiley, Chichester 2006. | DOI | MR
[23] Kurowicka, D.: Optimal truncation of vines. In: Dependence-Modeling - Handbook on Vine Copulas (D. Kurowicka and H. Joe, eds.), Word Scientific Publishing, Singapore 2011. | MR
[24] Lauritzen, S. L., Spiegelhalter, D. J.: Local Computations with probabilites on graphical structures and their application to expert systems. J. Roy. Statist. Soc. B 50 (1988), 157-227. | MR
[25] Lauritzen, S. L.: Graphical Models. Clarendon Press, Oxford 1996. | MR
[26] Szántai, T., Kovács, E.: Hypergraphs as a mean of discovering the dependence structure of a discrete multivariate probability distribution. In: Proc. Conference Applied Mathematical Programming and Modelling (APMOD), Bratislava 2008, Ann. Oper. Res. 193 (2012), 1, 71-90. | DOI | MR
[27] Whittaker, J.: Graphical Models in Applied Multivariate Statistics. John Wiley and Sons, 1990. | MR
Cité par Sources :