4.7 Article

Statistical inference of joint competing risks models from comparative bathtub shape distributions with hybrid censoring

期刊

ALEXANDRIA ENGINEERING JOURNAL
卷 86, 期 -, 页码 9-22

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ELSEVIER
DOI: 10.1016/j.aej.2023.11.051

关键词

Chen distributions; Bayes estimation; Symmetric loss function; Classical estimation; Competing risks model; Hybrid type-I censoring scheme; Joint censoring scheme

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In this study, the class of lifetime distributions with bathtub-shaped failure rate functions is examined. Statistical inference methods are used to estimate the parameters of the model, and the Bayesian approach is compared with classical methods. The study also discusses the evaluation of product relative merits based on lifetime duration under a hybrid censoring scheme.
In reliability engineering studies, the class of lifetime distributions with bathtub-shaped failure rate functions is particularly important since the lifetimes of electro-mechanical, electronic, and mechanical goods are frequently modelled with this characteristic. Comparative competing risk data are obtained in a variety of applications, including engineering, biological, medical, and other related fields. In this study, we adopted the statistical inference of joint distributions with a bathtub shape or increasing failure rate function (Chen distributions). The problem of determining the relative merits of products according to their lifetime duration is discussed under the hybrid type-I censoring scheme. Also, under consideration of independent causes of failure, a survival analysis and the assessment of one risk in the presence of other risks are discussed. The maximum likelihood (ML) method and the Bayes approach relative to the symmetric loss function are used to estimate the model parameters. Additionally, we build the classical confidence intervals (CIs) (asymptotic CI and bootstrap CI) to compare with the Bayes credible intervals. Moreover, theoretical results are evaluated and contrasted using a Monte Carlo simulation study. Finally, for illustration purposes, a real data set obtained from a laboratory experiment is evaluated using the suggested model with some brief comments.

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