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Statistical Analysis of Joint Progressive Censoring Data from Gompertz Distribution |
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PP: 759-780 |
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doi:10.18576/jsap/110302
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Author(s) |
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Fouzia B. Boulkeroua,
Omar M. Bdair,
Mohammad Z. Raqab,
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Abstract |
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| In this paper, we consider the problems of estimating the unknown parameters as well as predicting the failure times of the removed units in multiple stages of the joint progressively censored sample coming from two Gompertz distributions. The likelihood, Bootstrap and Bayesian methods are applied for the estimation problem. In Bayesian contexts, the posterior densities are estimated by using Lindley’s approximation, importance sampling and Metropolis-Hastings methods under different loss error functions. The confidence intervals based on the asymptotic normality and credible intervals based the Bayesian approach are discussed as well. A real life data is analyzed for illustrative purposes and Monte Carlo simulations are conducted to compare the performances of the all proposed methods.
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