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Research Article | Open Access

A new losses (revenues) probability model with entropy analysis, applications and case studies for value-at-risk modeling and mean of order-P analysis

Ibrahim Elbatal1L. S. Diab1Anis Ben Ghorbal1Haitham M. Yousof2( )Mohammed Elgarhy3,4Emadeldin I. A. Ali5,6
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt
Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, AlSharkia, Egypt
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef 62521, Egypt
Department of Economics, College of Economics and Administrative Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Department of Mathematics, Statistics, and Insurance, Faculty of Business, Ain Shams University, Egypt
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Abstract

This study introduces the Inverse Burr-X Burr-XII (IBXBXII) distribution as a novel approach for handling asymmetric-bimodal claims and revenues. It explores the distribution's statistical properties and evaluates its performance in three contexts. The analysis includes assessing entropy, highlighting the distribution's significance in various fields, and comparing it to rival distributions using practical examples. The IBXBXII model is then applied to analyze risk indicators in actuarial data, focusing on bimodal insurance claims and income. Simulation analysis shows its preference for right-skewed data, making it suitable for mathematical modeling and actuarial risk assessments. The study emphasizes the IBXBXII model's versatility and effectiveness, suggesting it as a flexible framework for actuarial data analysis, particularly in cases of large samples and right-skewed data.

CLC number: 62F15, 62G20, 65C60

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AIMS Mathematics
Pages 7169-7211

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Cite this article:
Elbatal I, Diab LS, Ghorbal AB, et al. A new losses (revenues) probability model with entropy analysis, applications and case studies for value-at-risk modeling and mean of order-P analysis. AIMS Mathematics, 2024, 9(3): 7169-7211. https://doi.org/10.3934/math.2024350

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Received: 15 November 2023
Revised: 20 January 2024
Accepted: 25 January 2024
Published: 15 March 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)