Exploring a new two-parameter Archimedean copula: the Gumbel-Joe copula
Year 2024,
Volume: 53 Issue: 6, 1742 - 1758, 28.12.2024
Christophe Chesneau
,
Weaam Alhadlaq
Abstract
In this article, we present a novel Archimedean copula constructed from a unique strict generator function. It can be described as a two-parameter unification of the well-established Gumbel-Barnett and Joe copulas. The first part is devoted to its formulation, as well as those of the corresponding density, the conditional copula, and the Kendall distribution function. Graphs are also included to illustrate their shape behavior under different parameter configurations. In a second part, we examine some of its notable properties, with emphasis on the correlation properties. Practical applications are discussed in the final part. In particular, we use the maximum likelihood estimation method to determine the unknown parameters involved from the data and perform a simulation study to demonstrate the effectiveness of this approach. We also analyze a dataset to provide practical illustrations of copula behavior and potential.
Supporting Institution
King Saud University
Project Number
RSPD2024R1011
Thanks
This research was funded by Researchers Supporting Project number (RSPD2024R1011), King Saud University, Riyadh, Saudi Arabia.
References
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Sciences 15 (1), 49–87, 2023.
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Metrika 85, 913–926, 2022.
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dont les marges sont données, Can. J. Stat. 14 (2), 145–159, 1986.
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a flexible parametric model for survival analysis, Stat. Methods Med. Res. 29
(8), 2295–2306, 2020.
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Probab. 4 (2), 291–302, 1967.
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Assoc. 83 (403), 834–841, 1988.
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516–522, 1965.
Year 2024,
Volume: 53 Issue: 6, 1742 - 1758, 28.12.2024
Christophe Chesneau
,
Weaam Alhadlaq
Project Number
RSPD2024R1011
References
- [1] W. Alhadlaq and A. Alzaid, Distribution function, probability generating function and
archimedean generator, Symmetry 12 (12), 2108, 2020.
- [2] F. A. Alqallaf and D. Kundu, A bivariate inverse generalized exponential distribution
and its applications in dependent competing risks model, Communications in
Statistics-Simulation and Computation 51 (12), 7019–7036, 2022.
- [3] C. Amblard and S. Girard, A new extension of bivariate FGM copulas, Metrika 70
(1), 1–17, 2009.
- [4] K. P. Burnham and D. R. Anderson, Multimodel inference: understanding AIC and
BIC in model selection, Sociol. Methods Res. 33 (2), 261–304, 2004.
- [5] C. Chesneau, On a weighted version of the Gumbel-Barnett copula, Innovative Journal
of Mathematics (IJM) 1 (2), 1–13, 2022.
- [6] C. Chesneau, Extensions of Two Bivariate Strict Archimedean Copulas, Computational
Journal of Mathematical and Statistical Sciences 2 (2), 159–180, 2023.
- [7] C. Chesneau, Parametric extensions of some referenced two-dimensional strict
Archimedean copulas, Research and Communications in Mathematics and Mathematical
Sciences 15 (1), 49–87, 2023.
- [8] E. A. Coelho-Barros, J. A. Achcar, J. Mazucheli, et al., Bivariate Weibull distributions
derived from copula functions in the presence of cure fraction and censored data, J.
Data Sci. 14 (2), 2016.
- [9] W. Diaz and C. M. Cuadras, An extension of the GumbelBarnett family of copulas,
Metrika 85, 913–926, 2022.
- [10] M. Franco, J.-M. Vivo, and D. Kundu, A generator of bivariate distributions: Properties,
estimation, and applications, Mathematics 8 (10), 1776, 2020.
- [11] C. Genest and R. J. MacKay, Copules Archimédiennes et families de lois bidimensionnelles
dont les marges sont données, Can. J. Stat. 14 (2), 145–159, 1986.
- [12] D. Ghosh, On the Plackett Distribution with Bivariate Censored Data, Int. J. Biostat.
4 (1), 2008.
- [13] E. J. Gumbel, Distributions des valeurs extremes en plusiers dimensions, Publ. Inst.
Statist. Univ. Paris 9, 171–173, 1960.
- [14] W. J. Huster, R. Brookmeyer, and S. G. Self, Modelling paired survival data with
covariates, Biometrics pages 145–156, 1989.
- [15] T. P. Hutchinson and C. D. Lai. Continuous bivariate distributions emphasising
applications. Technical report, 1990.
- [16] H. Joe, Parametric families of multivariate distributions with given margins, J. Multivar.
Anal. 46 (2), 262–282, 1993.
- [17] H. Joe, Multivariate models and multivariate dependence concepts, CRC press, 1997.
- [18] H. Joe, Dependence modeling with copulas, CRC press, 2014.
- [19] M. Jones, A. Noufaily, and K. Burke, A bivariate power generalized Weibull distribution:
a flexible parametric model for survival analysis, Stat. Methods Med. Res. 29
(8), 2295–2306, 2020.
- [20] A. W. Marshall and I. Olkin, A generalized bivariate exponential distribution, J. Appl.
Probab. 4 (2), 291–302, 1967.
- [21] A. W. Marshall and I. Olkin, Families of multivariate distributions, J. Am. Stat.
Assoc. 83 (403), 834–841, 1988.
- [22] R. B. Nelsen, An introduction to copulas, Springer Science & Business Media, 2007.
- [23] R. L. Plackett, A class of bivariate distributions, J. Am. Stat. Assoc. 60 (310),
516–522, 1965.