On Step-Stress Partially Accelerated Life Testing with Competing Risks Under Progressive Type-II Censoring

Q1 Decision Sciences

Annals of Data Science Pub Date : 2022-10-22 DOI:10.1007/s40745-022-00454-0

Sara O. Abd El-Azeem, Mahmoud H. Abu-Moussa, Moustafa M. Mohie El-Din, Lamiaa S. Diab

引用次数: 0

Abstract

In this article, step-stress partially accelerated life testing (SSPALT) with competing risks is studied when the lifetime of test units follows Nadarajah–Haghighi (NH) distribution. The maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are derived under progressive Type-II censoring. Furthermore, the approximate and credible confidence intervals (CIs) of the parameters are computed. A numerical example has been constructed to illustrate the methods used for the study. Finally, simulation studies are performed to demonstrate the accuracy of the MLEs and BEs for the parameters of Nadarajah–Haghighi distribution and the BEs showed better results than MLEs.

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具有竞争风险的阶跃应力部分加速寿命试验

本文研究了当测试单元的寿命服从 Nadarajah-Haghighi (NH) 分布时，具有竞争风险的阶跃应力部分加速寿命测试（SSPALT）。在渐进式 II 型普查条件下，得出了模型参数的最大似然估计值 (MLE) 和贝叶斯估计值 (BE)。此外，还计算了参数的近似可信置信区间（CI）。我们构建了一个数值示例来说明研究中使用的方法。最后，进行了模拟研究以证明 Nadarajah-Haghighi 分布参数的 MLEs 和 BEs 的准确性，BEs 显示出比 MLEs 更好的结果。

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

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.