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Bayesian Analysis of the Kumaraswamy Distribution Based on Fuzzy Data |
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PP: 1589-1601 |
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doi:10.18576/jsap/130603
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Author(s) |
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M. Seham,
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Abstract |
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| In this paper, we discussed the fuzzy Bayesian estimation method for the Kumaraswamy distribution (KD) parameters and the reliability function based on the progressive type-II fuzzy order statistics. The Bayesian estimators have been derived by Monte Carlo Integration (MCI), Markov Chain Monte Carlo (MCMC), and Tierney-Kadane (TK). These estimators are compared with the exact Bayesian estimators, via an intensive Monte Carlo simulation. The simulation results indicated that the Monte Carlo Integration and Markov Chain Monte Carlo methods provide better estimators and outperform the other estimators. Finally, two real datasets are provided to illustrate the results.
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