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Hanieh Panahi 《Journal of applied statistics》2014,41(1):215-232
This article deals with the statistical inference and prediction on Burr Type XII parameters based on Type II censored sample. It is observed that the maximum likelihood estimators (MLEs) cannot be obtained in closed form. We use the expectation-maximization algorithm to compute the MLEs. We also obtain the Bayes estimators under symmetric and asymmetric loss functions such as squared error and Linex By applying Lindley's approximation and Markov chain Monte Carlo (MCMC) technique. Further, MCMC samples are used to calculate the highest posterior density credible intervals. Monte Carlo simulation study and two real-life data-sets are presented to illustrate all of the methods developed here. Furthermore, we obtain a prediction of future order statistics based on the observed ordered because of its important application in different fields such as medical and engineering sciences. A numerical example carried out to illustrate the procedures obtained for prediction of future order statistics. 相似文献
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Hanieh Panahi 《Journal of applied statistics》2017,44(14):2575-2592
In this paper, the statistical inference of the unknown parameters of a Burr Type III (BIII) distribution based on the unified hybrid censored sample is studied. The maximum likelihood estimators of the unknown parameters are obtained using the Expectation–Maximization algorithm. It is observed that the Bayes estimators cannot be obtained in explicit forms, hence Lindley's approximation and the Markov Chain Monte Carlo (MCMC) technique are used to compute the Bayes estimators. Further the highest posterior density credible intervals of the unknown parameters based on the MCMC samples are provided. The new model selection test is developed in discriminating between two competing models under unified hybrid censoring scheme. Finally, the potentiality of the BIII distribution to analyze the real data is illustrated by using the fracture toughness data of the three different materials namely silicon nitride (Si3N4), Zirconium dioxide (ZrO2) and sialon (Si6?xAlxOxN8?x). It is observed that for the present data sets, the BIII distribution has the better fit than the Weibull distribution which is frequently used in the fracture toughness data analysis. 相似文献
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