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Hesham M. Reyad

Publications (4)

E-Bayesian Estimation of Two-Component Mixture of Inverse Lomax Distribution Based on Type-I Censoring Scheme

Hesham M. Reyad & Soha A. Othman · Journal of Advances in Mathematics and Computer Science · 2018

This study is concerned with comparing the E-Bayesian and Bayesian methods for estimating the shape parameters of two-component mixture of inverse Lomax distribution based on type-i censored data. Based on the squared error loss (SELF), minimum expected loss (MELF), Degroot loss...

Open access Research Article 10.9734/JAMCS/2018/39087

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 · 2017

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 balance...

Open access Research Article 10.9734/JAMCS/2017/34540

The Topp-Leone Burr-XII Distribution: Properties and Applications

Hesham M. Reyad & Soha A. Othman · Journal of Advances in Mathematics and Computer Science · 2017

In this paper we introduce a new generalization of the Burr-XII distribution using the genesis of the Topp-Leone distribution and is named as Topp-Leone Burr-XII (TLBXII) distribution. The statistical properties of this distribution including the mean, variance, coefficient of va...

Open access Research Article 10.9734/BJMCS/2017/33053

QE-Bayesian and E-Bayesian Estimation of the Frechet Model

Hesham M. Reyad, Adil M. Younis & Soha O. Ahmed · Journal of Advances in Mathematics and Computer Science · 2016

This paper proposes a new technique namely QE-Bayesian estimation, which is a new modification to the E-Bayesian method of estimation. The suggested approach based on replacing the quasi-likelihood function instead of the likelihood function in the E-Bayesian technique. This stud...

Open access Research Article 10.9734/BJMCS/2016/29231