Bayesian Estimation and Prediction of the Rayleigh Distribution Based on Ordered Ranked Set Sampling under Type-II Doubly Censored Samples

2021 ◽  
Vol 8 (2) ◽  
pp. 83-95
Keyword(s):  
Bayesian Estimation ◽  
Rayleigh Distribution ◽  
Ranked Set Sampling ◽  
Type Ii ◽  
Estimation And Prediction ◽  
Censored Samples ◽  
Doubly Censored

Reliability Estimation for Cold Standby Series System Based on General Progressive Type II Censored Samples

2013 ◽  
Vol 321-324 ◽  
pp. 2460-2463 ◽  
Author(s):  
Yi Min Shi ◽  
Xiao Lin Shi
Keyword(s):  
Bayesian Estimation ◽  
Reliability Function ◽  
Reliability Estimation ◽  
Likelihood Method ◽  
Series System ◽  
Type Ii ◽  
Censored Samples ◽  
Progressive Type ◽  
Cold Standby ◽  
Two Parameter

Suppose that the life of unit is distributed as two-parameter exponential distribution. The Bayesian estimation for cold standby series system is studied based on general Progressive type II censored samples. Under the different error loss, the Bayesian estimation of the unknown parameter and reliability function are derived where hyper-parameters are estimated by using Maximum likelihood method. At last, a numerical example is given by means of the Monte-Carlo simulation to illustrate the correctness and feasibility for the method proposed in this paper.

scholarly journals Bayesian Estimation and Prediction Based on Progressively First Failure Censored Scheme from a Mixture of Weibull and Lomax Distributions

2020 ◽  
pp. 357-372
Author(s):  
Mohamed M. Mahmoud ◽  
Manal Mohamed Nassar ◽  
Marwa Ahmed Aefa
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Likelihood Estimation ◽  
Bayes Estimation ◽  
Maximum Likelihood Estimates ◽  
Monte Carlo Simulation Study ◽  
Estimation And Prediction ◽  
Censored Samples ◽  
Symmetric Square

This paper develops Bayesian estimation and prediction, for a mixture of Weibull and Lomax distributions, in the context of the new life test plan called progressive first failure censored samples. Maximum likelihood  estimation and Bayes estimation, under informative and non-informative priors, are obtained using Markov Chain Monte Carlo methods, based on the symmetric square error Loss function and the asymmetric linear exponential (LINEX) and general entropy loss functions. The maximum likelihood estimates and the different Bayes estimates are compared via a Monte Carlo simulation study. Finally, Bayesian prediction intervals for future observations are obtained using a numerical example

Sign in / Sign up

Export Citation Format

Share Document