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Showing 2 results for Tail Dependence
Mohammad Amini, Hadi Jabbari Noughabi, Mahla Ghasemnejad Farsangi,
Volume 6, Issue 2 (2-2013)
Abstract
In this paper, three new non-parametric estimator for upper tail dependence measure are introduced and it is shown that these estimators are consistent and asymptotically unbiased. Also these estimators are compared using the Mont Carlo simulation of three different copulas and present a new method in order to select the best estimator by applying the real data.
Mrs. Elaheh Kadkhoda, Mr. Gholam Reza Mohtashami Borzadaran, Mr. Mohammad Amini,
Volume 18, Issue 1 (8-2024)
Abstract
Maximum entropy copula theory is a combination of copula and entropy theory. This method obtains the maximum entropy distribution of random variables by considering the dependence structure. In this paper, the most entropic copula based on Blest's measure is introduced, and its parameter estimation method is investigated. The simulation results show that if the data has low tail dependence, the proposed distribution performs better compared to the most entropic copula distribution based on Spearman's coefficient. Finally, using the monthly rainfall series data of Zahedan station, the application of this method in the analysis of hydrological data is investigated.
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