倒指数Pareto分布的可靠性估计

IF 2.3 2区工程技术 Q3 ENGINEERING, INDUSTRIAL

Quality Technology and Quantitative Management Pub Date : 2022-10-03 DOI:10.1080/16843703.2022.2125762

R. Kumari, Y. Tripathi, R. Sinha, Liang Wang

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引用次数: 1

摘要

摘要本文研究了在ii型截割下观测数据时多组分系统的可靠性估计问题。当应力和强度(SS)变量遵循具有共同尺度参数的逆指数分布时，推导出该参数函数的各种估计。首先得到了信度的最大似然估计。然后，根据渐近理论得到近似置信区间。进一步构造了有用的关键量，并推导了可靠性的备选估计。本文还研究了SS部件的所有参数都未知的情况，并对可靠性提出了各种估计。讨论了模型参数间的等价检验。使用模拟比较了所有估计的性能，并得出了评论。为了说明目的，我们分析了两个真实的数据集。

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Reliability estimation for the inverted exponentiated Pareto distribution

ABSTRACT We consider estimation of reliability in a multicomponent system when data are observed under Type-II censoring. Various estimates of this parametric function are derived when stress and strength (SS) variables follow inverse exponentiated distributions with a common scale parameter. We first obtain maximum likelihood estimate of the reliability. Then, approximate confidence intervals are obtained based on asymptotic theory. Further useful pivotal quantities are constructed and in sequel alternative estimates of the reliability are derived. The case where all parameters of SS components are unknown is also studied and various estimates for the reliability are proposed. Equivalence testing between model parameters is discussed as well. Performance of all estimates is compared using simulations and comments are derived. We analyze two real data sets for illustration purposes.

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来源期刊

Quality Technology and Quantitative Management ENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

5.10

自引率

21.40%

发文量

审稿时长

>12 weeks

期刊介绍： Quality Technology and Quantitative Management is an international refereed journal publishing original work in quality, reliability, queuing service systems, applied statistics (including methodology, data analysis, simulation), and their applications in business and industrial management. The journal publishes both theoretical and applied research articles using statistical methods or presenting new results, which solve or have the potential to solve real-world management problems.