Estimating the stress-strength parameter in multi-component systems based on adaptive hybrid progressive censoring

Akram Kohansal, Shirin Shoaee and Mohammad Z. Raqab

International Journal of Industrial and Systems Engineering, 2022, vol. 41, issue 3, 363-403

Abstract: Under different probability distributions, numerous authors have discussed the estimation of the reliability in a stress-strength model. In this study, we investigate the reliability parameter estimation in multi-component stress-strength models based on the adaptive hybrid progressive censored sample of two-parameter Kumaraswamy distribution in various situations. In this regard, various methods such as the maximum likelihood, approximate maximum likelihood, Lindley's Bayesian, and Metropolis-Hastings methods are used to estimate the reliability parameter in this structure. Furthermore, the corresponding confidence intervals, bootstrap confidence intervals, and highest posterior density credible intervals of the multi-component reliability parameter are then established. Also, simulation studies are represented to evaluate and compare the performance of the proposed methods and one practical dataset to analyse illustrative purposes.

Keywords: adaptive type-II hybrid censored sample; Bayesian estimation; Kumaraswamy distribution; Monte Carlo simulation; multi-component stress-strength model; progressive censored sample. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:41:y:2022:i:3:p:363-403

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