Sara O. Abd El-Azeem, Mahmoud H. Abu-Moussa, Moustafa M. Mohie El-Din, Lamiaa S. Diab
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On Step-Stress Partially Accelerated Life Testing with Competing Risks Under Progressive Type-II Censoring
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.
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.
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