ii型截割下双参数指数恒应力加速寿命试验模型的区间估计

IF 2.3 2区工程技术 Q3 ENGINEERING, INDUSTRIAL

Quality Technology and Quantitative Management Pub Date : 2022-12-26 DOI:10.1080/16843703.2022.2147688

Wenhui Wu, B. Wang, Jiayan Chen, Jiuzhou Miao, Qingyuan Guan

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引用次数: 2

摘要

本文研究了ii型截割条件下恒应力加速寿命试验数据的双参数指数分布区间估计问题。假设尺度和位置参数的寿命应力模型为对数线性模型。得到了模型参数的极大似然估计量。给出了尺度参数寿命应力模型中各参数的精确置信区间和近似置信区间。导出了定位参数寿命-应力模型中参数的广义置信区间，以及设计应力水平下的均值和可靠度函数。通过蒙特卡洛仿真对所提出的广义置信区间的性能进行了评价。最后，用一个实际的例子来说明我们的方法。

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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Interval estimation of the two-parameter exponential constant stress accelerated life test model under Type-II censoring

ABSTRACT In this paper, interval estimation of the two-parameter exponential distribution based on constant-stress accelerated life test data under Type-II censoring is studied. The life-stress models of the scale and location parameters are assumed to be log-linear. The maximum likelihood estimators of the model parameters are obtained. The exact and approximate confidence intervals for the parameters in the life-stress model of the scale parameter are developed. The generalized confidence intervals for the parameters in the life-stress model of the location parameter, and mean and reliability function at the designed stress level are derived. The performance of the proposed generalized confidence intervals is assessed by the Monte Carlo simulation. Finally, a real example is used to illustrate our methods.

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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.