scholarly journals Bayesian Estimation and Prediction for the Maxwell Failure Distribution Based on Type II Censored Data

2016 ◽  
Vol 06 (01) ◽  
pp. 49-60 ◽  
Author(s):  
Anwar M. Hossain ◽  
Gabriel Huerta
Keyword(s):  
Bayesian Estimation ◽  
Censored Data ◽  
Type Ii ◽  
Estimation And Prediction ◽  
Failure Distribution ◽  
Type Ii Censored Data

scholarly journals Estimation and Prediction for the Poisson-Exponential Distribution Based on Type-II Censored Data

2018 ◽  
Vol 38 (1) ◽  
pp. 96-115 ◽  
Author(s):  
R. Arabi Belaghi ◽  
Mehri Noori Asl ◽  
Ozlem Gurunlu Alma ◽  
Sukhdev Singh ◽  
Mahdi Vasfi
Keyword(s):  
Exponential Distribution ◽  
Censored Data ◽  
Type Ii ◽  
Estimation And Prediction ◽  
Type Ii Censored Data

scholarly journals Bayesian Estimation of Entropy for Burr Type XII Distribution under Progressive Type-II Censored Data

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 313
Author(s):  
Xinjing Wang ◽  
Wenhao Gui
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Censored Data ◽  
Loss Functions ◽  
Type Ii ◽  
Important Indicator ◽  
Progressive Type ◽  
Type Ii Censored Data ◽  
Highest Posterior Density ◽  
Burr Type Xii

With the rapid development of statistics, information entropy is proposed as an important indicator used to quantify information uncertainty. In this paper, maximum likelihood and Bayesian methods are used to obtain the estimators of the entropy for a two-parameter Burr type XII distribution under progressive type-II censored data. In the part of maximum likelihood estimation, the asymptotic confidence intervals of entropy are calculated. In Bayesian estimation, we consider non-informative and informative priors respectively, and asymmetric and symmetric loss functions are both adopted. Meanwhile, the posterior risk is also calculated to evaluate the performances of the entropy estimators against different loss functions. In a numerical simulation, the Lindley approximation and the Markov chain Monte Carlo method were used to obtain the Bayesian estimates. In turn, the highest posterior density credible intervals of the entropy were derived. Finally, average absolute bias and mean square error were used to evaluate the estimators under different methods, and a real dataset was selected to illustrate the feasibility of the above estimation model.

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