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Stress-strength reliability estimation for bivariate copula function with rayleigh marginals

Author

Listed:
  • A. James

    (Ramanujan School of Mathematical Sciences, Pondicherry University)

  • N. Chandra

    (Ramanujan School of Mathematical Sciences, Pondicherry University)

  • Nicy Sebastian

    (St Thomas College)

Abstract

In the classical stress-strength reliability model, the majority of the contributions focus on estimating system reliability with independent assumptions of stress and strength. In many real applications, such an assumption is violated because more or less dependence relations exist. Therefore, attempting dependent stress-strength reliability modelling is interesting. In this article, we assume stress and strength are linked by Fralie–Gumble–Morgenstern copula with Rayleigh marginals as the underlying distribution. The estimates of reliability and dependence parameters are obtained by using maximum likelihood estimation, inference function margin, and semi-parametric methods. In addition, the length of confidence interval and coverage probability of the dependence parameter are also reported. The performance of the proposed methods is shown by using Monte-Carlo simulation as well as three distinct real-life data sets.

Suggested Citation

  • A. James & N. Chandra & Nicy Sebastian, 2023. "Stress-strength reliability estimation for bivariate copula function with rayleigh marginals," 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. 14(1), pages 196-215, March.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01836-6
    DOI: 10.1007/s13198-022-01836-6
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    References listed on IDEAS

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    More about this item

    Keywords

    Dependence stress-strength reliability; Rayleigh distribution; Fralie–Gumble–Morgenstern copula; Maximum likelihood estimation; Inference function margins; Semi-parametric method;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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