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A Generalized FGM Copula with Enhanced Negative Dependence for Hydrological Applications | ||
| Journal of Advances in Statistical Learning and Data Analysis | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 مهر 1405 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.30473/jaslda.2026.77901.1003 | ||
| نویسنده | ||
| Hakim Bekrizadeh* | ||
| Department of Statistics, Payame Noor University, Tehran, Iran, | ||
| چکیده | ||
| In this paper, we generalize the Farlie–Gumbel–Morgenstern (FGM) bivariate distribution function by incorporating an appropriate weighting function, thereby extending the FGM copula. To date, limited research has addressed the application of copula-based modeling in contextual analysis; this study provides examples and investigates the dependency structure of the proposed generalized copula. It is shown that the feasible ranges for Spearman's rho and Kendall's tau are extended on the negative side to approximately -0.75 and -0.50, respectively, while the upper bounds remain at 0.33 and 0.22. Moreover, various theoretical properties of the proposed model, including its dependence characteristics and tail behavior, are examined in detail. Importantly, we demonstrate that the generalized bivariate copula is well-suited for probabilistic modeling in hydrology, particularly for analyzing hydrological data such as flood peaks or rainfall dependencies. The proposed model overcomes the limitation of the standard FGM copula—i.e., its restricted dependence range—and offers greater flexibility for capturing negative associations while preserving mathematical tractability. Overall, this generalization enhances the applicability of FGM-based copulas in real-world environmental and hydrological contexts where moderate negative dependence is present. | ||
| کلیدواژه‌ها | ||
| FGM copula؛ weighted distributions؛ measures of dependence؛ concepts of dependence؛ real data | ||
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آمار تعداد مشاهده مقاله: 3 |
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