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Estimation of reliability of multicomponent stress–strength for a Kumaraswamy distribution

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  • Sanku Dey
  • Josmar Mazucheli
  • M. Z. Anis

Abstract

This article deals with the Bayesian and non Bayesian estimation of multicomponent stress–strength reliability by assuming the Kumaraswamy distribution. Both stress and strength are assumed to have a Kumaraswamy distribution with common and known shape parameter. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach and the results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets are analyzed for illustrative purposes.

Suggested Citation

  • Sanku Dey & Josmar Mazucheli & M. Z. Anis, 2017. "Estimation of reliability of multicomponent stress–strength for a Kumaraswamy distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1560-1572, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:4:p:1560-1572
    DOI: 10.1080/03610926.2015.1022457
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    Citations

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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.
    2. Devendra Pratap Singh & Mayank Kumar Jha & Yogesh Mani Tripathi & Liang Wang, 2023. "Inference on a Multicomponent Stress-Strength Model Based on Unit-Burr III Distributions," Annals of Data Science, Springer, vol. 10(5), pages 1329-1359, October.
    3. Hassan S. Bakouch & Tassaddaq Hussain & Marina Tošić & Vladica S. Stojanović & Najla Qarmalah, 2023. "Unit Exponential Probability Distribution: Characterization and Applications in Environmental and Engineering Data Modeling," Mathematics, MDPI, vol. 11(19), pages 1-22, October.
    4. M. S. Kotb & M. Z. Raqab, 2021. "Estimation of reliability for multi-component stress–strength model based on modified Weibull distribution," Statistical Papers, Springer, vol. 62(6), pages 2763-2797, December.
    5. Liang Wang & Huizhong Lin & Kambiz Ahmadi & Yuhlong Lio, 2021. "Estimation of Stress-Strength Reliability for Multicomponent System with Rayleigh Data," Energies, MDPI, vol. 14(23), pages 1-23, November.
    6. Yuhlong Lio & Tzong-Ru Tsai & Liang Wang & Ignacio Pascual Cecilio Tejada, 2022. "Inferences of the Multicomponent Stress–Strength Reliability for Burr XII Distributions," Mathematics, MDPI, vol. 10(14), pages 1-28, July.

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