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Dr. Mohamed Sewalim El-sayed Hamed :: Publications:

Title:
The Statistical Distributional Validation under a Novel Generalized Gamma Distribution with Value-at-Risk Analysis for the Historical Claims, Censored and Uncensored Real-life Applications
Authors: Yousof, H. M., Ali, E. I. A., Aidi, K., Butt, N. S., Saber, M. M., Al-Nefaie, A. H., Aljadani, A., Mansour, M. M., Hamed, M. S., & Ibrahim, M.
Year: 2025
Keywords: Characterizations; Distributional Validation; Nikulin-Rao-Robson; Risk Assessment; Value-at-risk; Statistical Modeling.
Journal: Pakistan Journal of Statistics and Operation Research (PJSOR)
Volume: 21
Issue: 1
Pages: 51-69
Publisher: Pakistan Journal of Statistics and Operation Research (PJSOR)
Local/International: International
Paper Link:
Full paper Mohamed Sewalim El-sayed Hamed_The Statistical Distributional Validation under a Novel Generalized Gamma Distribution.pdf
Supplementary materials Not Available
Abstract:

This study introduces and examines a new probability distribution, presenting various characterizations. Key financial risk measures, including the value-at-risk (VaR), tail-value-at-risk (TVaR), also referred to as conditional tail expectation or conditional value-at-risk (CVaR), tail variance (TV), tail mean-variance (TMV), and mean excess loss (MExL) function are evaluated using maximum likelihood estimation, ordinary least squares, weighted least squares, and the Anderson-Darling estimation methods. These methods are applied for actuarial analysis in both a simulation study and an insurance claims data application. For validation of the distribution using complete data, the widely recognized Nikulin-Rao-Robson statistic is utilized and assessed through simulations and three real data sets. Two uncensored real-life data sets for failure times and remission times are used in uncensored validation. Additionally, for censored data validation, a modified version of the Nikulin-Rao-Robson statistic is proposed and evaluated through extensive simulations and three censored real data sets.

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