极端冲击模型的随机排序

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Sirous Fathi Manesh, Muhyiddin Izadi, Baha-Eldin Khaledi
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引用次数: 0

摘要

在通常的冲击模型中,冲击来自单一来源。Bozbulut and Eryilmaz[2020]。广义极端冲击模型及其应用。统计通讯-模拟与计算[49(1):110-120]在冲击来自$m\geq 1$可能来源之一时介绍了两种类型的极端冲击模型。在模型1中,随着时间的推移,冲击来自不同的来源。在模型2中,最初,冲击随机地来自$m$源之一,并且冲击继续从同一源到达。本文证明了在通常的随机排序下,模型1的寿命小于模型2。我们进一步证明,如果冲击到达间隔时间具有递增的故障率分布,则通常的随机排序可以推广到危害率排序。我们使用多数化的概念研究了模型2的寿命相对于冲击严重程度的随机行为。我们应用新的随机排序结果表明,模型1下的年龄替代政策比模型2下的成本更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On stochastic ordering among extreme shock models
In the usual shock models, the shocks arrive from a single source. Bozbulut and Eryilmaz [(2020). Generalized extreme shock models and their applications. Communications in Statistics – Simulation and Computation49(1): 110–120] introduced two types of extreme shock models when the shocks arrive from one of $m\geq 1$ possible sources. In Model 1, the shocks arrive from different sources over time. In Model 2, initially, the shocks randomly come from one of $m$ sources, and shocks continue to arrive from the same source. In this paper, we prove that the lifetime of Model 1 is less than Model 2 in the usual stochastic ordering. We further show that if the inter-arrival times of shocks have increasing failure rate distributions, then the usual stochastic ordering can be generalized to the hazard rate ordering. We study the stochastic behavior of the lifetime of Model 2 with respect to the severity of shocks using the notion of majorization. We apply the new stochastic ordering results to show that the age replacement policy under Model 1 is more costly than Model 2.
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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