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Research Article Open access CC BY 4.0

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 · pp. 1–22 · Published 5 Feb 2018

10.9734/JAMCS/2018/39087

Abstract

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 (DLF), precautionary loss (PLF), LINEX loss (LLF) and entropy loss (ELF) functions, formulas of E-Bayesian and Bayesian estimations are given. These estimates are derived based on a conjugate gamma prior and uniform hyperprior distributions. 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 corresponding Bayesian estimates.

Bayesian estimates E-Bayesian estimates inverse Lomax distribution loss functions mixture models.

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