Volume 6, Issue 4 (Vol. 6, No. 4, 2020)
mmr 2020, 6(4): 563-578 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Piriaei H, Yari G, Farnoosh R. E-Bayesian Estimations of Reliability and Hazard Rate in the Generalized Inverted Exponential Distribution based on Type II Censoring. mmr 2020; 6 (4) :563-578
URL: http://mmr.khu.ac.ir/article-1-2856-en.html
URL: http://mmr.khu.ac.ir/article-1-2856-en.html
1- Department of Statistics, Science and Research branch, Islamic Azad University, Tehran, Iran
2- Iran University of Science and Technology, School of Mathematics, Tehran, Iran ,yari@iust.ac.ir
3- Iran University of Science and Technology, School of Mathematics, Tehran, Iran
2- Iran University of Science and Technology, School of Mathematics, Tehran, Iran ,
3- Iran University of Science and Technology, School of Mathematics, Tehran, Iran
Abstract: (2858 Views)
Introduction
This paper is concerned with using the Maximum Likelihood, Bayes and a new method, E-Bayesian, estimations for computing estimates for the unknown parameter, reliability and hazard rate functions of the Generalized Inverted Exponential distribution. The estimates are derived based on a conjugate prior for the unknown parameter. E-Bayesian estimations are obtained based on three different prior distributions of the hyper parameters. Asymptotic behaviors of E-Bayesian estimations and relations among them have been discussed. The results are computed based on type-II censoring and squared error loss function. Finally, a comparison among the Maximum Likelihood, Bayes and E-Bayesian estimation methods in different sample sizes are made, using the Monte Carlo simulation, which shows that the new method is more efficient than other old methods and is easy to operate.
Method
Suppose the Generalized Inverted Exponential distribution and its unknown parameter, reliability and hazard rate functions. Then the estimates of functions of interest are derived based on type II censored samples of this distribution, using the Monte Carlo simulation.
Results
Results show that the E-Bayesian method is more efficient than other old methods and is easy to operate. Also, the asymptotic behaviors of three E-Bayesian estimations are the same../files/site1/files/64/7.pdf
This paper is concerned with using the Maximum Likelihood, Bayes and a new method, E-Bayesian, estimations for computing estimates for the unknown parameter, reliability and hazard rate functions of the Generalized Inverted Exponential distribution. The estimates are derived based on a conjugate prior for the unknown parameter. E-Bayesian estimations are obtained based on three different prior distributions of the hyper parameters. Asymptotic behaviors of E-Bayesian estimations and relations among them have been discussed. The results are computed based on type-II censoring and squared error loss function. Finally, a comparison among the Maximum Likelihood, Bayes and E-Bayesian estimation methods in different sample sizes are made, using the Monte Carlo simulation, which shows that the new method is more efficient than other old methods and is easy to operate.
Method
Suppose the Generalized Inverted Exponential distribution and its unknown parameter, reliability and hazard rate functions. Then the estimates of functions of interest are derived based on type II censored samples of this distribution, using the Monte Carlo simulation.
Results
Results show that the E-Bayesian method is more efficient than other old methods and is easy to operate. Also, the asymptotic behaviors of three E-Bayesian estimations are the same../files/site1/files/64/7.pdf
Keywords: E-Bayesian estimation, Type-II censoring, Reliability, Hazard Rate, Monte Carlo simulation
Type of Study: Research Paper |
Subject:
stat
Received: 2018/10/15 | Revised: 2021/02/20 | Accepted: 2019/05/11 | Published: 2021/01/29 | ePublished: 2021/01/29
Received: 2018/10/15 | Revised: 2021/02/20 | Accepted: 2019/05/11 | Published: 2021/01/29 | ePublished: 2021/01/29
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
