E-Bayesian Estimation For Some Characteristics Of Weibull Generalized Exponential Progressive Type-II Censored Samples

TL;DR

This paper introduces an E-Bayesian estimation approach for Weibull Generalized Exponential distribution parameters under censored data, demonstrating improved efficiency over traditional methods through simulations and real data analysis.

Contribution

It develops a novel E-Bayesian estimation method for censored Weibull Generalized Exponential data, exploring different priors and loss functions, and compares it with existing estimation techniques.

Findings

E-Bayesian estimators outperform maximum likelihood and Bayesian methods in efficiency.

Different priors significantly influence the estimations.

The method is validated through Monte Carlo simulations and real data analysis.

Abstract

Estimation of reliability and hazard rate is one of the most important problems raised in many applications especially in engineering studies as well as human lifetime. In this regard, different methods of estimation have been used. Each method exploits various tools and suffers from problems such as complexity of computations, low precision, and so forth. This study is employed the E-Bayesian method, for estimating the parameter and survival functions of the Weibull Generalized Exponential distribution. The estimators are obtained under squared error and LINEX loss functions under progressive type-II censored samples. E-Bayesian estimations are derived based on three priors of hyperparameters to investigate the influence of different priors on estimations. The asymptotic behaviours of E-Bayesian estimations have been investigated as well as relationships among them. Finally, a comparison among the maximum likelihood, Bayes, and E-Bayesian estimations are made, using real data and Monte Carlo simulation. Results show that the new method is more efficient than previous methods.