Bayesian estimation for randomly censored generalized exponential distribution under asymmetric loss functions |
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Authors: | Muhammad Yameen Danish Muhammad Aslam |
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Affiliation: | 1. Department of Mathematics and Statistics , Allama Iqbal Open University , Islamabad , Pakistan;2. Department of Statistics , Quaid-i-Azam University , Islamabad , Pin 45320 , Pakistan |
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Abstract: | This paper deals with the Bayesian estimation of generalized exponential distribution in the proportional hazards model of random censorship under asymmetric loss functions. It is well known for the two-parameter lifetime distributions that the continuous conjugate priors for parameters do not exist; we assume independent gamma priors for the scale and the shape parameters. It is observed that the closed-form expressions for the Bayes estimators cannot be obtained; we propose Tierney–Kadane's approximation and Gibbs sampling to approximate the Bayes estimates. Monte Carlo simulation is carried out to observe the behavior of the proposed methods and one real data analysis is performed for illustration. Bayesian methods are compared with maximum likelihood and it is observed that the Bayes estimators perform better than the maximum-likelihood estimators in some cases. |
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Keywords: | Bayes estimate log-concave density function Gibbs sampling Tierney–Kadane's approximation Markov chain Monte Carlo |
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