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Research Article Open access CC BY 4.0

E-Bayesian and Hierarchical Bayesian Estimations Based on Dual Generalized Order Statistics from the Inverse Weibull Model

Hesham M. Reyad, Adil M. Younis, Soha A. Othman

Journal of Advances in Mathematics and Computer Science · pp. 1–29 · Published 28 Jun 2017

10.9734/JAMCS/2017/34540

Abstract

This paper is devoted to compare the E-Bayesian and hierarchical Bayesian estimations of the scale parameter corresponding to the inverse Weibull distribution based on dual generalized order statistics. The E-Bayesian and hierarchical Bayesian estimates are obtained under balanced squared error loss function (BSELF), precautionary loss function (PLF), entropy loss function (ELF) and Degroot loss function (DLF). The properties of the E-Bayesian and hierarchical Bayesian estimates are investigated. Comparisons among all estimates are performed in terms of absolute bias (ABias) and mean square error (MSE) via Monte Carlo simulation. Numerical computations showed that E-Bayesian estimates are more efficient than the hierarchical Bayesian estimates.

E-Bayesian estimates inverse Weibull distribution hierarchical Bayesian estimates loss functions Monte Carlo simulation.

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