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一个带有精算度量及应用的扩展瑞利-威布尔模型。

An extended Rayleigh Weibull model with actuarial measures and applications.

作者信息

Elgarhy Mohammed, Johannssen Arne, Kayid Mohamed

机构信息

Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef 62521, Egypt.

Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, AlSharkia, Egypt.

出版信息

Heliyon. 2024 May 31;10(11):e32143. doi: 10.1016/j.heliyon.2024.e32143. eCollection 2024 Jun 15.

DOI:10.1016/j.heliyon.2024.e32143
PMID:39668969
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11637057/
Abstract

In this article, we discuss a new extension of the Rayleigh-Weibull model using the Marshall-Olkin family of distributions. The proposed model is called the Marshall-Olkin-Rayleigh-Weibull (MORW) model. Various statistical properties of the MORW distribution are discussed, including explicit expressions for quantiles, moments, incomplete and conditional moments, some inequality measures, moment generating function, moments of the residual and reversed residual life, the Rényi entropy, and order statistics. Six different estimation methods are considered to investigate the behavior of the model parameters within the MORW model. A Monte Carlo simulation study is conducted to evaluate the performance of these different estimators. In addition, some actuarial measures, such as the Value-at-Risk, the expected shortfall, the tail Value-at-Risk, the tail variance, the tail variance premium, the tail conditional expectation, and the tail standard deviation premium, are calculated. Finally, applying the model to real data sets illustrates the applicability and usefulness of the MORW distribution.

摘要

在本文中,我们讨论了一种使用马歇尔 - 奥尔金分布族对瑞利 - 威布尔模型的新扩展。所提出的模型称为马歇尔 - 奥尔金 - 瑞利 - 威布尔(MORW)模型。讨论了MORW分布的各种统计特性,包括分位数、矩、不完全矩和条件矩的显式表达式、一些不等式度量、矩生成函数、剩余寿命和反向剩余寿命的矩、雷尼熵以及顺序统计量。考虑了六种不同的估计方法来研究MORW模型中模型参数的行为。进行了蒙特卡罗模拟研究以评估这些不同估计量的性能。此外,还计算了一些精算度量,如风险价值、预期短缺、尾部风险价值、尾部方差、尾部方差溢价、尾部条件期望和尾部标准差溢价。最后,将该模型应用于实际数据集说明了MORW分布的适用性和实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/4183d3016fcd/gr020.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/a283718302b0/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/4c194e781313/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/4236f5286b92/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/620ed9a7f98a/gr009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/3ec78cbc4530/gr010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/f9bab105f8dd/gr011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/d0b94a8c4e40/gr012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/f1d0b2a5d650/gr014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/de2bc310be84/gr015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/81ee916f5543/gr016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/98896f0c9bad/gr017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/c7c485128a3f/gr018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/c0fbd4663128/gr019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd70/11637057/4183d3016fcd/gr020.jpg

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