Modeling directional monotonicity with copulas

Document Type : Research Paper

Authors

1 Department of Mathematics. Almeria University

2 Research Group of Theory of Copulas and Applications, University of Almer´ıa, Spain

3 Department of Applied Mathematics, Granada, Spain

4 Department of Mathematics,, University of Almeria, Carretera de Sacramento s/n, 04120 Almeria, Spain

Abstract

The purpose of this paper is to characterize the concept of monotonicity according to a direction related to a set of n random variables in terms of its associated n-copula. We start establishing relationships in the bivariate and trivariate cases, which will help to understand the extension to the multivariate case. Several examples are provided.

Keywords

Main Subjects


[1] M. M. Ali, N. N. Mikhail, M. S. Haq, A class of bivariate distributions including the bivariate logistic, Journal of
Multivariate Analysis, 8 (1978), 405-412. https://doi.org/10.1016/0047-259X(78)90063-5
[2] A. Dolati, E. Mokhtari, A. Dastbaravarde, Aspects of conditional symmetry and asymmetry of copulas, Iranian
Journal of Fuzzy Systems, 21(6) (2024), 1-13. https://doi.org/10.22111/IJFS.2024.45725.8061
[3] N. Doodman, M. Amini, H. Jabbari, A. Dolati, FGM generated Archimedean copulas with concave multiplicative
generators, Iranian Journal of Fuzzy Systems, 18(2) (2021), 15-29. https://doi.org/10.22111/IJFS.2021.5911
[4] F. Durante, C. Sempi, Principles of copula theory, Chapman and Hall/CRC, Boca Raton, 2016. https://doi.org/
10.1201/b18674
[5] M. Esfahani, M. Amini, G. R. Mohtashami-Borzadaran, A. Dolati, A new copula-based bivariate Gompertz-Makeham
model and its application to COVID-19 mortality data, Iranian Journal of Fuzzy Systems, 20(3) (2023), 159-175.
https://doi.org/10.22111/IJFS.2023.7645
[6] N. I. Fisher, Copulas, in: S. Kotz, C. B. Read, D. L. Banks (Eds.), Encyclopedia of Statistical Sciences, Vol. 1,
Wiley, New York, (1997), 159-163.
[7] J. C. Fodor, M. Roubens, Fuzzy preference modelling and multicriteria decision support, Kluwer, Dordrecht, 1994.
https://doi.org/10.1007/978-94-017-1648-2
[8] P. H´ajek, Metamathematics of fuzzy logic, Dordrecht, Kluwer, 1998. https://doi.org/10.1007/
978-94-011-5300-3
[9] R. Harris, A multivariate definition for increasing hazard rate distribution function, The Annals of Mathematical
Statistics, 41 (1970), 713-717. https://doi.org/10.1214/aoms/1177697121
[10] H. Joe, Multivariate models and dependence concepts, Chapman and Hall, London, 1997. https://doi.org/10.
1201/9780367803896
[11] A. M¨uller, M. Scarsini, Archimedean copulae and positive dependence, Journal of Multivariate Analysis, 93 (2006),
434-445. https://doi.org/10.1016/j.jmva.2004.04.003
[12] J. Navarro, F. Pellerey, M. A. Sordo, Weak dependence notions and their mutual relationships, Mathematics, 9
(2021), Article 81. https://doi.org/10.3390/math9010081
[13] R. B. Nelsen, An introduction to copulas, Second Edition, Springer, New York, 2006. https://doi.org/10.1007/
0-387-28678-0
[14] J. J. Quesada-Molina, M. ´Ubeda-Flores, Monotonic random variables according to a direction, Axioms, 13 (2024),
Article 275. https://doi.org/10.3390/axioms13040275
[15] A. Sklar, Fonctions de r´epartition `a n dimensions et leurs marges, Publications de l’Institut de Statistique de
l’Universit´e de Paris, 8 (1959), 229-231. http://doi.org/10.2139/ssrn.4198458
[16] M. ´Ubeda-Flores, J. Fern´andez-S´anchez, Sklar’s theorem: The cornerstone of the theory of copulas, in: M. ´Ubeda-
Flores, E. de Amo Artero, F. Durante, J. Fern´andez S´anchez (Eds.), Copulas and Dependence Models with Applications,
Springer, Cham, (2017), 241-258. https://doi.org/10.1007/978-3-319-64221-5
[17] Z. Wei, T. Wang, W. Panichkitkosolkul, Dependence and association concepts through copulas, in: V. N. Huynh,
V. Kreinovich, S. Sriboonchitta (Eds.), Modeling Dependence in Econometrics - Advances in Intelligent Systems
and Computing, Vol. 251, Springer, Cham, (2014), 113-126. https://doi.org/10.1007/978-3-319-03395-2