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Objective Bayesian analysis for generalized exponential stress–strength model

Author

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  • Sang Gil Kang

    (Sangji University)

  • Woo Dong Lee

    (Daegu Haany University)

  • Yongku Kim

    (Kyungpook National University)

Abstract

In reliability studies, a stress–strength model is often used to analyze a system that fails whenever the applied stress is greater than the strength. Statistical inference of reliability is widely used in a number of areas, such as engineering, clinical trials, and quality control. In addition to the common stress–strength model with one stress and one strength, the reliability of more complex systems has also been studied. In this study, we consider the reliability of a generalized stress–strength model that consists of a serial system with one stress and multiple strengths. We then develop the probability matching priors and reference priors for a generalized exponential stress–strength model. We demonstrate that the two-group reference prior and Jeffreys prior are not a matching prior. Through a simulation study and real data example, we also demonstrate that the proposed probability matching priors match the target coverage probabilities in a frequentist sense even for a small sample size.

Suggested Citation

  • Sang Gil Kang & Woo Dong Lee & Yongku Kim, 2021. "Objective Bayesian analysis for generalized exponential stress–strength model," Computational Statistics, Springer, vol. 36(3), pages 2079-2109, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01083-6
    DOI: 10.1007/s00180-021-01083-6
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    References listed on IDEAS

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    1. Sanjib Basu & Rama Lingham, 2003. "Bayesian estimation of system reliability in Brownian stress-strength models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(1), pages 7-19, March.
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    Cited by:

    1. Shubham Saini & Renu Garg, 2022. "Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples," Computational Statistics, Springer, vol. 37(4), pages 1795-1837, September.

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