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Inference on the stress-strength reliability of multi-component systems based on progressive first failure censored samples

Author

Listed:
  • Akram Kohansal
  • Carlos J Pérez-González
  • Arturo J Fernández

Abstract

This paper studies the statistical estimation of the stress-strength reliability of multi-component systems under the progressive first failure censoring samples, where the lifetime distribution of each component follows the modified Kumaraswamy distribution. Both the point and interval estimations of the parameters in the reliability function are considered. To this aim, some estimations such as maximum likelihood estimation (MLE), asymptotic confidence intervals, uniformly minimum variance unbiased estimation (UMVUE), approximate Bayes estimation, and highest posterior density (HPD) intervals are obtained. By employing the Monte Carlo simulation, comparison of the performance between different estimates is provided. The paper then analyzes a case study for illustration of the proposed method.

Suggested Citation

  • Akram Kohansal & Carlos J Pérez-González & Arturo J Fernández, 2024. "Inference on the stress-strength reliability of multi-component systems based on progressive first failure censored samples," Journal of Risk and Reliability, , vol. 238(5), pages 1053-1073, October.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:5:p:1053-1073
    DOI: 10.1177/1748006X231188075
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    References listed on IDEAS

    as
    1. 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.
    2. Akram Kohansal & Shirin Shoaee, 2021. "Bayesian and classical estimation of reliability in a multicomponent stress-strength model under adaptive hybrid progressive censored data," Statistical Papers, Springer, vol. 62(1), pages 309-359, February.
    3. 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.
    4. Akram Kohansal, 2019. "On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample," Statistical Papers, Springer, vol. 60(6), pages 2185-2224, December.
    5. Fatih Kızılaslan & Mustafa Nadar, 2018. "Estimation of reliability in a multicomponent stress–strength model based on a bivariate Kumaraswamy distribution," Statistical Papers, Springer, vol. 59(1), pages 307-340, March.
    Full references (including those not matched with items on IDEAS)

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