Bayesian analysis for Type-II hybrid censored sample from inverse Weibull distribution

2013 ◽  
Vol 4 (3) ◽  
pp. 241-248 ◽  
Author(s):  
Sanjay Kumar Singh ◽  
Umesh Singh ◽  
Vikas Kumar Sharma
Keyword(s):  
Weibull Distribution ◽  
Bayesian Analysis ◽  
Type Ii ◽  
Inverse Weibull Distribution ◽  
Censored Sample

scholarly journals Estimation of the Residuals Entropy Function of Inverse Weibull Distribution Based on Generalized Type-II Hybrid Censored Samples

2021 ◽  
pp. 21-34
Author(s):  
Moshera A. M. Ahmad
Keyword(s):  
Information Theory ◽  
Weibull Distribution ◽  
Residual Entropy ◽  
Type Ii ◽  
Simulation Experiments ◽  
Censored Samples ◽  
Inverse Weibull Distribution ◽  
Censored Sample ◽  
Generalized Type ◽  
Two Parameter

Shannon’s entropy plays important role in the information theory. However, it can’t be applied to systems which have survived for some time. Therefore, the concept of residual entropy was developed. In this paper, the estimation of the entropy of a two-parameter inverse Weibull distribution based on the generalized type-II hybrid censored sample is considered. The Bayes estimator for the residual entropy of the Inverse Weibull distribution under the generalized type-II hybrid censored sample is given. Simulation experiments are conducted to see the effectiveness of the different estimators.

scholarly journals Bayesian Estimation for Inverse Weibull Distribution Under Progressive Type-II Censored Data With Beta-Binomial Removals

2018 ◽  
Vol 47 (1) ◽  
pp. 77-94
Author(s):  
Pradeep Kumar Vishwakarma ◽  
Arun Kaushik ◽  
Aakriti Pandey ◽  
Umesh Singh ◽  
Sanjay Kumar Singh
Keyword(s):  
Weibull Distribution ◽  
Real Data ◽  
Estimation Procedure ◽  
Type Ii ◽  
Unknown Parameters ◽  
Data Set ◽  
Progressive Type ◽  
Inverse Weibull Distribution ◽  
Type Ii Censored Data ◽  
Censoring Scheme

This paper deals with the estimation procedure for inverse Weibull distribution under progressive type-II censored samples when removals follow Beta-binomial probability law. To estimate the unknown parameters, the maximum likelihood and Bayes estimators are obtained under progressive censoring scheme mentioned above. Bayes estimates are obtained using Markov chain Monte Carlo (MCMC) technique considering square error loss function and compared with the corresponding MLE's. Further, the expected total time on test is obtained under considered censoring scheme.  Finally, a real data set has been analysed to check the validity of the study.

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