Estimation based on progressive first-failure censoring from exponentiated exponential distribution |
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Authors: | Heba S. Mohammed Essam K. AL-Hussaini |
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Affiliation: | 1. Mathematics Department, Faculty of Science, Assiut University, New Valley Branch, Assiut, Egypt;2. Mathematical Science, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia;3. Faculty of Science, Alexandria University, Alexandria, Egypt |
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Abstract: | In this paper, point and interval estimations for the parameters of the exponentiated exponential (EE) distribution are studied based on progressive first-failure-censored data. The Bayes estimates are computed based on squared error and Linex loss functions and using Markov Chain Monte Carlo (MCMC) algorithm. Also, based on this censoring scheme, approximate confidence intervals for the parameters of EE distribution are developed. Monte Carlo simulation study is carried out to compare the performances of the different methods by computing the estimated risks (ERs), as well as Akaike's information criteria (AIC) and Bayesian information criteria (BIC) of the estimates. Finally, a real data set is introduced and analyzed using EE and Weibull distributions. A comparison is carried out between the mentioned models based on the corresponding Kolmogorov–Smirnov (K–S) test statistic to emphasize that the EE model fits the data with the same efficiency as the other model. Point and interval estimation of all parameters are studied based on this real data set as illustrative example. |
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Keywords: | Exponentiated exponential distribution progressive first-failure censoring Markov Chain Monte Carlo method |
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