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On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample

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Abstract

Based on progressively Type-II censored samples, this paper deals with the estimation of multicomponent stress-strength reliability by assuming the Kumaraswamy distribution. Both stress and strength are assumed to have a Kumaraswamy distribution with different the first shape parameters, but having the same second shape parameter. Different methods are applied for estimating the reliability. The maximum likelihood estimate of reliability is derived. Also its asymptotic distribution is used to construct an asymptotic confidence interval. The Bayes estimates of reliability have been developed by using Lindley’s approximation and the Markov Chain Monte Carlo methods due to the lack of explicit forms. The uniformly minimum variance unbiased and Bayes estimates of reliability are obtained when the common second shape parameter is known. The highest posterior density credible intervals are constructed for reliability. Monte Carlo simulations are performed to compare the performances of the different methods, and one data set is analyzed for illustrative purposes.

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Authors and Affiliations

  1. Department of Statistics, Imam Khomeini International University, Qazvin, Iran

    Akram Kohansal

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  1. Akram Kohansal
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Correspondence to Akram Kohansal.

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Kohansal, A. On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample. Stat Papers 60, 2185–2224 (2019). https://doi.org/10.1007/s00362-017-0916-6

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  • DOI: https://doi.org/10.1007/s00362-017-0916-6

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