Power-Modified Kies-Exponential Distribution: Properties,Classical and Bayesian Inference with an Application to Engineering Data |
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| Authors: | Ahmed Z. Afify Ahmed M. Gemeay Nada M. Alfaer Gauss M. Cordeiro Eslam H. Hafez |
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| Affiliation: | 1.Department of Statistics, Mathematics and Insurance, Benha University, Benha 13511, Egypt;2.Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt;3.Department of Mathematics & Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;4.Departamento de Estatística, Universidade Federal de Pernambuco, Recife 50710-165, Brazil;5.Mathematics Department, Faculty of Science, Helwan University, Helwan 11795, Egypt; |
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| Abstract: | We introduce here a new distribution called the power-modified Kies-exponential (PMKE) distribution and derive some of its mathematical properties. Its hazard function can be bathtub-shaped, increasing, or decreasing. Its parameters are estimated by seven classical methods. Further, Bayesian estimation, under square error, general entropy, and Linex loss functions are adopted to estimate the parameters. Simulation results are provided to investigate the behavior of these estimators. The estimation methods are sorted, based on partial and overall ranks, to determine the best estimation approach for the model parameters. The proposed distribution can be used to model a real-life turbocharger dataset, as compared with 24 extensions of the exponential distribution. |
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| Keywords: | Anderson– Darling estimation, Cramé r– von Mises estimation, exponential distribution, mean residual life, percentile estimation, power transformation, risk measures |
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