Stochastic Models in Probability and Statistics

Volume 1, Issue 1, January 0, Pages 17-36   

DOI: 10.22067/smps.2024.45178

References

1.  Ali, M. M., Mikhail, N. N. and Haq, M. S. (1978). A class of bivariate distributions including the bivariate logistic. Journal of Multivariate Analysis, 8, 405–412.
2.  Amini, M., Jabbari, H. and Borzadaran, G. R. M. (2011). Aspects of dependence in generalized Farlie-Gumbel-Morgenstern distributions. Communication in Statistics - Simulation and Computations, 40, 1192–1205.
3.  Arnold, B.C. and Ng, H. K. T. (2011). Flexible bivariate beta distributions. Journal of Multivariate Analysis, 102, 1194–1202.
4.  Bairamov, I. and Kotz, S. (2002). Dependence structure and symmetry of Huang-Kotz FGM distributions and their extensions. Metrika, 56, 55–72.
5.  Bairamov, I. G., Kotz, S. and Bekci, M. (2001). New generalized Farlie-Gumbel-Morgenstern distributions and concomitants of order statistics. Journal of Applied Statistics, 28, 521–536.
6.  Becherini, A., Martina, M. L. and Marchi, M. (2013). Copula-based statistical analysis of environmental data. Environmentalmetrics, 24, 1–11.
7.  Celebioglu, S. (1995). Yeni bir copulalar ailesi, Arastirma Sempozyumu. 95, Program ve Bildiriler, Ankara, 152-153, 27–29 Kasim.
8.  Celebioglu, S. (1997). A way of generating comprehensive copulas. Journal of the Institute of Science and Technology, 10, 57–61.
9.  Chesneau, C. (2021a). A new two-dimensional relation copula inspiring a generalized version of the Farlie-Gumbel-Morgenstern copula. Research and Communications in Mathematics and Mathematical Sciences, 13, 99–128.
10.  Chesneau, C. (2021b). On new types of multivariate trigonometric copulas. AppliedMath, 1, 3–17.
11.  Chesneau, C. (2022). On a weighted version of the Gumbel-Barnett copula. Innovative Journal of Mathematics (IJM), 1, 1–13.
12.  Chesneau, C. (2023). Theoretical validation of new two-dimensional one-variable-power copulas. Axioms, 12, 392.
13.  Domma, F. and Giordano, S. (2012). A copula-based approach to account for dependence in stress-strength models. Statistical Papers, 54, 807–826.
14. Durante, F. and Sempi, C. (2016). Principles of Copula Theory. CRS Press, Boca Raton.
15. El Ktaibi, F., Bentoumi, R., Sottocornola, N. and Mesfiui, M. (2022). Bivariate copulas based on counter-monotonic shock method. Risks, 10, 202.
16. Embrechts, P., Klüppelberg, C. and Mikosch, T. (2001). Modelling extremal events: for insurance and fiance (Vol. 33). Springer Science & Business Media.
17. Izadkhah, S., Ahmadzade, H. and Amini, M. (2015). Further results for a general family of bivariate copulas. Communications in Statistics-Theory and Methods, 44, 3146–157.
18. Jaworski, P. (2023). On copulas with a trapezoid support. Dependence Modeling, 11, 20230101.
19. Joe, H. (2015) Dependence Modeling with Copulas. CRS Press, Boca Raton.
20. Korkmaz, M. Ç. (2020). The unit generalized half normal distribution: A new bounded distribution with inference and application. UPB Scientifi Bulletin, Series A: Applied Mathematics and Physics, 82, 133–140.
21. Korkmaz, M. Ç. and Chesneau, C. (2021). On the unit Burr-XII distribution with the quantile regression modeling and applications. Computational and Applied Mathematics, 40, 1–26.
22. Martínez-Flórez, G., Lemonte, A. J., Moreno-Arenas, G. and Tovar-Falón, R. (2022). The bivariate unit-sinhnormal distribution and its related regression model. Mathematics, 10, 3125.
23. Mazucheli, J., Menezes, A. F. B. and Dey, S. (2019). Unit-Gompertz distribution with applications. Statistica, 79, 25–43.
24. Michimae, H. and Emura, T. (2022). Likelihood inference for copula models based on left truncated and competing risks data from field studies. Mathematics, 10, 2163.
25. Mirhosseini, S. M., Amini, M. and Dolati, A. (2015). On a general structure of the bivariate FGM type distributions. Applications of Mathematics, 60, 91–108.
26.  Morgenstern, D. (1956). Einfache Beispiele zweidimensionaler Verteilungen. Mitteilungsblatt für Mathematische Statistik, 8, 234–235.
27. Nadarajah, S., Shih, S.H. and Nagar, D. K. (2017). A new bivariate beta distribution. Statistics, 51, 455–474.
    Nelsen, R. B. (2006). An Introduction to Copulas. 2nd ed. Springer Science+Business Media, Inc.: Berlin, Germany.
    R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria. Available at: https://www.R-project.org.
28. Rodríguez-Lallena, J. A. and Úbeda-Flores, M. (2004). A new class of bivariate copulas. Statistics and Probability Letters, 66, 315–325.
29. Shih, J.-H., Konno, Y., Chang, Y.-T. and Emura, T. (2022). Copula-based estimation methods for a common mean vector for bivariate meta-analyses. Symmetry, 14, 186.
30. Sklar, A. (1959). Fonctions de repartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229–231.
31. Susam, S. O. (2020a). Parameter estimation of some Archimedean copulas based on minimum Cramér-von-Mises distance. Journal of The Iranian Statistical Society, 19, 163–183.
32. Susam, S. O. (2020b). A new family of Archimedean copula via trigonometric generator function. Gazi University Journal of Science, 33, 806–813.
33. Taketomi, N., Yamamoto, K., Chesneau, C. and Emura, T. (2022). Parametric distributions for survival and reliability analyses, a review and historical sketch. Mathematics, 10, 3907.
34. Yeh, C.-T., Liao, G.-Y. and Emura, T. (2023). Sensitivity analysis for survival prognostic prediction with gene selection: A copula method for dependent censoring. Biomedicines, 11, 797.