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Modeling bivariate distributions with triangular fuzzy data and its application in hydrological studies: A copula-based approach | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 3، مرداد و شهریور 2026، صفحه 1-13 اصل مقاله (485.4 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.51406.9082 | ||
| نویسندگان | ||
| Parisa Khalilpoor1؛ Abbas Parchami* 2؛ Reza Pourmousa3 | ||
| 1Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2عضو هییت علمی | ||
| 3Department of Statistics, Faculty of Mathematics ~and Computer Shahid Bahonar University of Kerman Kerman, Iran | ||
| چکیده | ||
| Fuzzy data analysis presents significant computational challenges due to its inherent ambiguity and uncertainty. Traditional statistical methods do not have the capability to effectively capture and model the uncertainty in fuzzy observations. A novel approach is proposed in this paper to model unknown bivariate densities using fuzzy observations and incorporating the dependency between variables. By employing this copula-based approach, we have effectively managed the computational complexity associated with the analysis of fuzzy data. The proposed approach has been applied to model groundwater aquifers distribution. | ||
| کلیدواژهها | ||
| bivariate density estimation؛ fuzzy-valued data؛ FGM copula؛ AMH copula؛ Gaussian copula | ||
| مراجع | ||
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