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Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples

Author

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  • Shubham Saini

    (University of Delhi)

  • Renu Garg

    (University of Delhi)

Abstract

In this article, the problem of reliability inference of multicomponent stress–strength (MSS) from Kumaraswamy-G (Kw-G) family of distributions under progressive first failure censoring is considered. The reliability of MSS is considered when both the stress and strength variables follow Kw-G distributions with different first shape parameters and common second shape parameter. The maximum likelihood (ML) and Bayes estimators of reliability are derived when all the parameters are unknown. Also, the ML, uniformly minimum variance unbiased and Bayes estimators of reliability are derived in case of common shape parameter is known. The Bayesian credible and HPD credible intervals of reliability are developed using Gibbs sampling method. The performance of various estimates developed are discussed by a Monte Carlo simulation study. At last, two real life examples are considered for illustrative purposes.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:4:d:10.1007_s00180-021-01180-6
    DOI: 10.1007/s00180-021-01180-6
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    References listed on IDEAS

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