Nesreen M. Al-Olaimat , Husam A. Bayoud , Mohammad Z. Raqab

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The Kies probability model was proposed as an alternative to the extendedWeibull models as it provides a more efficient fit to some real-life data sets in comparison to the aforementioned models. The paper proposes classical and Bayesian inferences for the Kies distribution based on records. Maximum likelihood estimates are studied jointly with asymptotic and bootstrap confidence intervals. Moreover, Bayes estimates, along with credible intervals are discussed assuming squared and LINEX loss functions. The proposed estimation methods have been investigated and compared via simulation studies. A real data set has been analysed for illustrative purposes.


Bayesian estimates, Kies distribution, maximum likelihood estimation, records


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