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Showing 3 results for Jabbari
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.
Bibi Maryam Taheri, Hadi Jabbari, Mohammad Amini,
Volume 16, Issue 1 (9-2022)
Abstract
Paying attention to the copula function in order to model the structure of data dependence has become very common in recent decades. Three methods of estimation, moment method, mixture method, and copula moment, are considered to estimate the dependence parameter of copula function in the presence of outlier data. Although the moment method is an old method, sometimes this method leads to inaccurate estimation. Thus, two other moment-based methods are intended to improve that old method. The simulation study results showed that when we use copula moment and mixture moment for estimating the dependence parameter of copula function in the presence of outlier data, the obtained MSEs are smaller. Also, the copula moment method is the best estimate based on MSE. Finally, the obtained numerical results are used in a practical example.
Tara Mohammadi, Hadi Jabbari, Sohrab Effati,
Volume 19, Issue 1 (9-2025)
Abstract
Support vector machine (SVM) as a supervised algorithm was initially invented for the binary case, then due to its applications, multi-class algorithms were also designed and are still being studied as research. Recently, models have been presented to improve multi-class methods. Most of them examine the cases in which the inputs are non-random, while in the real world, we are faced with uncertain and imprecise data. Therefore, this paper examines a model in which the inputs are uncertain and the problem's constraints are also probabilistic. Using statistical theorems and mathematical expectations, the problem's constraints have been removed from the random state. Then, the moment estimation method has been used to estimate the mathematical expectation. Using Monte Carlo simulation, synthetic data has been generated and the bootstrap resampling method has been used to provide samples as input to the model and the accuracy of the model has been examined. Finally, the proposed model was trained with real data and its accuracy was evaluated with statistical indicators. The results from simulation and real examples show the superiority of the proposed model over the model based on deterministic inputs.
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