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

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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.

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Acknowledgements

The authors are very grateful to the Editor-in-Chief and two anonymous referees for their valuable suggestions, which lead to the improved version of the earlier manuscript. The first author, Mr. Shubham Saini is very thankful to the Council of Scientific & Industrial Research (CSIR), Ministry of Science and Technology, Government of India for their financial support in the form of Junior Research fellowship (09/045(1614)/2018-EMR-I).

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Authors and Affiliations

  1. Department of Statistics, University of Delhi, Delhi, 110007, India

    Shubham Saini

  2. Department of Statistics, Kirori Mal College, University of Delhi, Delhi, 110007, India

    Renu Garg

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  1. Shubham Saini
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  2. Renu Garg
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Saini, S., Garg, R. Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples. Comput Stat 37, 1795–1837 (2022). https://doi.org/10.1007/s00180-021-01180-6

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  • DOI: https://doi.org/10.1007/s00180-021-01180-6

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