Orderings of extremes among dependent extended Weibull random variables

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Ramkrishna Jyoti Samanta, Sangita Das, N. Balakrishnan
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引用次数: 0

Abstract

In this work, we consider two sets of dependent variables Abstract Image$\{X_{1},\ldots,X_{n}\}$ and Abstract Image$\{Y_{1},\ldots,Y_{n}\}$, where Abstract Image$X_{i}\sim EW(\alpha_{i},\lambda_{i},k_{i})$ and Abstract Image$Y_{i}\sim EW(\beta_{i},\mu_{i},l_{i})$, for Abstract Image$i=1,\ldots, n$, which are coupled by Archimedean copulas having different generators. We then establish different inequalities between two extremes, namely, Abstract Image$X_{1:n}$ and Abstract Image$Y_{1:n}$ and Abstract Image$X_{n:n}$ and Abstract Image$Y_{n:n}$, in terms of the usual stochastic, star, Lorenz, hazard rate, reversed hazard rate and dispersive orders. Several examples and counterexamples are presented for illustrating all the results established here. Some of the results here extend the existing results of [5] (Barmalzan, G., Ayat, S.M., Balakrishnan, N., & Roozegar, R. (2020). Stochastic comparisons of series and parallel systems with dependent heterogeneous extended exponential components under Archimedean copula. Journal of Computational and Applied Mathematics 380: Article No. 112965).

依存扩展 Weibull 随机变量的极值排序
在这项工作中,我们考虑两组因变量 ${X_{1},\ldots,X_{n}\}$ 和 $\{Y_{1},\ldots,Y_{n}\}$ ,其中 $X_{i}\sim EW(\alpha_{i}、\和 $Y_{i}\sim EW(\beta_{i},\mu_{i},l_{i})$, for $i=1,\ldots, n$,它们由具有不同生成器的阿基米德共线耦合。然后,我们根据通常的随机阶、星阶、洛伦兹阶、危险率阶、反向危险率阶和分散阶,在两个极值,即 $X_{1:n}$ 和 $Y_{1:n}$ 以及 $X_{n:n}$ 和 $Y_{n:n}$ 之间建立不同的不等式。本文列举了几个例子和反例,以说明本文建立的所有结果。其中一些结果扩展了 [5] 的现有结果(Barmalzan, G., Ayat, S.M., Balakrishnan, N., & Roozegar, R. (2020)。阿基米德 copula 下具有依赖异质扩展指数成分的串联和并联系统的随机比较。计算与应用数学杂志》380:文章编号:112965)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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