Previous |  Up |  Next

Article

Title: On the connection between cherry-tree copulas and truncated R-vine copulas (English)
Author: Kovács, Edith
Author: Szántai, Tamás
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 53
Issue: 3
Year: 2017
Pages: 437-460
Summary lang: English
.
Category: math
.
Summary: Vine copulas are a flexible way for modeling dependences using only pair-copulas as building blocks. However if the number of variables grows the problem gets fastly intractable. For dealing with this problem Brechmann at al. proposed the truncated R-vine copulas. The truncated R-vine copula has the very useful property that it can be constructed by using only pair-copulas and a lower number of conditional pair-copulas. In our earlier papers we introduced the concept of cherry-tree copulas. In this paper we characterize the relation between cherry-tree copulas and truncated R-vine copulas. It turns out that the concept of cherry-tree copula is more general than the concept of truncated R-vine copula. Although both contain in their expressions conditional independences between the variables, the truncated R-vines constructed in greedy way do not exploit the existing conditional independences in the data. We give a necessary and sufficient condition for a cherry-tree copula to be a truncated R-vine copula. We introduce a new method for truncated R-vine modeling. The new idea is that in the first step we construct the top tree by exploiting conditional independences for finding a good-fitting cherry-tree of order $k$. If this top tree is a tree in an R-vine structure then this will define a truncated R-vine at level $k$ and in the second step we construct a sequence of trees which leads to it. If this top tree is not a tree in an R-vine structure then we can transform it into such a tree at level $k+1$ and then we can again apply the second step. The second step is performed by a backward construction named Backward Algorithm. This way the cherry-tree copulas always can be expressed by pair-copulas and conditional pair-copulas. (English)
Keyword: copula
Keyword: conditional independences
Keyword: Regular-vine
Keyword: truncated vine
Keyword: cherry-tree copula
MSC: 60C05
MSC: 62H05
idZBL: Zbl 06819617
idMR: MR3684679
DOI: 10.14736/kyb-2017-3-0437
.
Date available: 2017-11-12T09:41:27Z
Last updated: 2018-01-10
Stable URL: http://hdl.handle.net/10338.dmlcz/146936
.
Reference: [1] Aas, K., Czado, C., Frigessi, A., Bakken, H.: Pair-copula constructions of multiple dependence..Insur. Math. Econom. 44 (2009), 182-198. MR 2517884, 10.1016/j.insmatheco.2007.02.001
Reference: [2] Acar, E. F., Genest, C., Nešlehová, J.: Beyond simplified pair-copula constructions..J. Multivariate Anal. 110 (2012), 74-90. MR 2927510, 10.1016/j.jmva.2012.02.001
Reference: [3] Bauer, A., Czado, C., Klein, T.: Pair-copula construction for non-Gaussian DAG models..Canad. J. Stat. 40 (2012), 1, 86-109. MR 2896932, 10.1002/cjs.10131
Reference: [4] Bedford, T., Cooke, R.: Probability density decomposition for conditionally dependent random variables modeled by vines..Ann. Math. Artif. Intell. 32 (2001), 245-268. MR 1859866, 10.1023/a:1016725902970
Reference: [5] Bedford, T., Cooke, R.: Vines - a new graphical model for dependent random variables..Ann. Statist. 30 (2002), 4, 1031-1068. MR 1926167, 10.1214/aos/1031689016
Reference: [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. MR 2896931, 10.1002/cjs.10141
Reference: [7] Bukszár, J., Prékopa, A.: Probability bounds with cherry trees..Math. Oper. Res. 26 (2001), 174-192. MR 1821836, 10.1287/moor.26.1.174.10596
Reference: [8] Bukszár, J., Szántai, T.: Probability bounds given by hypercherry trees..Optim. Methods Software 17 (2002), 409-422. MR 1944289, 10.1080/1055678021000033955
Reference: [9] Cover, T. M., Thomas, J. A.: Elements of Information Theory..Wiley Interscience, New York 1991. MR 1122806, 10.1002/0471200611
Reference: [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. MR 3051264, 10.1007/978-3-642-12465-5_4
Reference: [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. MR 3000041, 10.1016/j.csda.2012.08.010
Reference: [12] Hanea, A., Kurowicka, D., Cooke, R.: Hybrid method for quantifying and analyzing Bayesian belief networks..Qual. Reliab. Engrg. 22 (2006), 708-729. 10.1002/qre.808
Reference: [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. MR 2595309, 10.1016/j.jmva.2009.12.001
Reference: [14] Haff, I. Hobaek, Segers, J.: Nonparametric estimation of pair-copula constructions with the empirical pair-copula..2010.
Reference: [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. MR 3504845, 10.1016/j.csda.2016.03.003
Reference: [16] Joe, H.: Multivariate Models and Dependence Concepts..Chapman and Hall, London 1997. Zbl 0990.62517, MR 1462613, 10.1201/b13150
Reference: [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. MR 2681735, 10.1007/978-3-642-03735-1_3
Reference: [18] Kovács, E., Szántai, T.: Multivariate copula expressed by lower dimensional copulas..2010.
Reference: [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.
Reference: [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).
Reference: [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. 10.1109/wsc.2002.1172895
Reference: [22] Kurowicka, D., Cooke, R. M.: Uncertainty Analysis with High Dimensional Dependence Modelling..John Wiley, Chichester 2006. MR 2216540, 10.1002/0470863072
Reference: [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 2856976
Reference: [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 0964177
Reference: [25] Lauritzen, S. L.: Graphical Models..Clarendon Press, Oxford 1996. MR 1419991
Reference: [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. MR 2874757, 10.1007/s10479-010-0814-y
Reference: [27] Whittaker, J.: Graphical Models in Applied Multivariate Statistics..John Wiley and Sons, 1990. MR 1112133
.

Files

Files Size Format View
Kybernetika_53-2017-3_4.pdf 1.752Mb application/pdf View/Open
Back to standard record
Partner of
EuDML logo