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Parametric and nonparametric estimation stress strength model based on copula function under first-failure progressively unified hybrid censoring schemes

Author

Listed:
  • Junmei Jia

    (Inner Mongolia University of Technology)

  • Hongyan Fan

    (Inner Mongolia University of Technology)

  • Cen Zhang

    (Inner Mongolia University of Technology)

Abstract

In the traditional interference of stress strength model, it is generally supposed that the strength variable and the stress variable are independent. However, in many engineering applications strength variable and the stress variable are dependent. To solve the situation where the strength variable and the stress variable are dependent, in this paper the dependent between strength and stress is considered, the correlation between strength and stress is described by Farlie–Gumbel–Morgenstern copula function. Three estimate methods (maximum likelihood estimation, Bayes estimator, kernel density estimation) are used to estimate the reliability for $$\delta =P(X

Suggested Citation

  • Junmei Jia & Hongyan Fan & Cen Zhang, 2024. "Parametric and nonparametric estimation stress strength model based on copula function under first-failure progressively unified hybrid censoring schemes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(12), pages 5700-5712, December.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:12:d:10.1007_s13198-024-02571-w
    DOI: 10.1007/s13198-024-02571-w
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    References listed on IDEAS

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    1. Filippo Domma & Sabrina Giordano, 2012. "A stress–strength model with dependent variables to measure household financial fragility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 375-389, August.
    2. Alessandro Barbiero, 2012. "Interval estimators for reliability: the bivariate normal case," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 501-512, June.
    3. Debasis Kundu & Rameshwar D. Gupta, 2005. "Estimation of P[Y > X] for generalized exponential distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 61(3), pages 291-308, June.
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