Distributional Censored and Uncensored Validation Testing under a Modified Test Statistic with Risk Analysis and Assessment

Yusra Tashkandy, Walid Emam, Gauss M. Cordeiro, M. Masoom Ali, Khaoula Aidi, Haitham M. Yousof and Mohamed Ibrahim

Journal of Mathematics, 2023, vol. 2023, 1-17

Abstract:

This paper introduces and studies a unique probability distribution. The maximum likelihood estimation, the ordinary least squares, the weighted least squares, and the Anderson–Darling estimation methods all take into account a number of financial risk indicators, including the value-at-risk, tail-value-at-risk, tail variance, tail mean-variance, and mean excess loss function. These four approaches were used in a simulation study and an application to insurance claims data for the actuarial evaluation. The well-known Nikulin–Rao–Robson statistic is taken into consideration for distributional validation under the whole set of data. Three complete actual datasets and a simulation study are used to evaluate the Nikulin–Rao–Robson test statistic. An updated version of the Nikulin–Rao–Robson statistic is taken into consideration for censored distributional validation. Three censored actual datasets and a thorough simulation analysis are used to evaluate the novel Nikulin–Rao–Robson test statistic.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:8852528

DOI: 10.1155/2023/8852528

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