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Approximating Bayes Estimates by Means of the Tierney Kadane, Importance Sampling and Metropolis-Hastings within Gibbs Methods in the Poisson-Exponential Distribution: A Comparative Study
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Firozeh Bastan    , Seyyed Mohamad Taghi Kamel Mirmostafaee * |
| University of Mazandaran , m.mirmostafaee@umz.ac.ir |
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Abstract: (7609 Views) |
| Here, we work on the problem of point estimation of the parameters of the Poisson-exponential distribution through the Bayesian and maximum likelihood methods based on complete samples. The point Bayes estimates under the symmetric squared error loss (SEL) function are approximated using three methods, namely the Tierney Kadane approximation method, the importance sampling method and the Metropolis-Hastings within Gibbs algorithm. The interval estimators are also obtained. The performance of the point and interval estimators are compared with each other by means of a Monte Carlo simulation. Several conclusions are given at the end. |
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| Keywords: Bayesian inference, Importance sampling method, Metropolis-Hastings within Gibbs algorithm, Monte Carlo simulation, Poisson-exponential distribution, Tierney Kadane approximation. |
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Full-Text [PDF 812 kb]
(10917 Downloads)
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Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2019/07/25 | Accepted: 2019/10/30 | Published: 2019/12/13
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