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

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  • Akram Kohansal
  • Shirin Shoaee
  • Mohammad Z. Raqab

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.

Suggested Citation

  • Akram Kohansal & Shirin Shoaee & Mohammad Z. Raqab, 2022. "Estimating the stress-strength parameter in multi-component systems based on adaptive hybrid progressive censoring," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 41(3), pages 363-403.
    • Handle: RePEc:ids:ijisen:v:41:y:2022:i:3:p:363-403
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