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Showing 3 results for Jomhoori
Masoud Ajami, Vaheed Fakoor, Sara Jomhoori,
Volume 5, Issue 1 (9-2011)
Abstract
In sampling, arisen data with probability proportional to its length is called Length-bised. Nonparametric density estimation in length-biased sampling is more difficult than other states. One of the famous estimators in this context is the one introduced by Jones (1991). In this paper, we calculate the bandwidth parameter of this estimator by Bayes'method. The strong consistency of this estimator have been proved with a random Bandwidth. We have compared the performance of Bayes'method with cross validation by using simulation studies.
Atefe Pourkazemi, Hadi Alizadeh Noughabi, Sara Jomhoori,
Volume 13, Issue 2 (2-2020)
Abstract
In this paper, the Bootstrap and Jackknife methods are stated and using these methods, entropy is estimated. Then the estimators based on Bootstrap and Jackknife are investigated in terms of bias and RMSE using simulation. The proposed estimators are compared with other entropy estimators by Monte Carlo simulation. Results show that the entropy estimators based on Bootstrap and Jackknife have a good performance as compared to the other estimators. Next, some tests of normality based on the proposed estimators are introduced and the power of these tests are compared with other tests.
Seyed Jamal, Khorashadizadeh, Fatemeh Yousefzadeh, Sara Jomhoori,
Volume 19, Issue 2 (4-2025)
Abstract
Researchers develop generalized families of distributions to better model data in fields like risk management, economics, and insurance. In this paper, a new distribution, the Extended Exponential Log-Logistic Distribution, is introduced, which belongs to the class of heavy-tailed distributions. Some statistical properties of the model, including moments, moment-generating function, entropy, and economic inequality curves, are derived. Six estimation methods are proposed for estimating the model parameters, and the performance of these methods is evaluated using randomly generated datasets. Additionally, several insurance-related measures, including Value at Risk, Tail Value at Risk, Tail Variance, and Tail Variance Premium, are calculated. Finally, two real insurance datasets are employed, showing that the proposed model fits the data better than many existing related models.
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