Bayesian Estimation Based on Record Values from Exponentiated Weibull Distribution: An Markov Chain Monte Carlo Approach
| Autor: | Rashad M. EL-Sagheer |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Bayes estimator
Monte Carlo method Estimator Markov chain Monte Carlo General Medicine Statistics::Computation Bayes' theorem symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Statistics Prior probability symbols Maximum a posteriori estimation Statistics::Methodology Applied mathematics Exponentiated Weibull distribution Exponentiated Weibull distribution (EWD) Record values Bootstrap methods Bayes estimation Gibbs and Metropolis sampler Mathematics |
| Zdroj: | American Journal of Theoretical and Applied Statistics. 4:26 |
| ISSN: | 2326-8999 |
| DOI: | 10.11648/j.ajtas.20150401.15 |
| Popis: | In this paper, we consider the Bayes estimators of the unknown parameters of the exponentiated Weibull distribution (EWD) under the assumptions of gamma priors on both shape parameters. Point estimation and confidence intervals based on maximum likelihood and bootstrap methods are proposed. The Bayes estimators cannot be obtained in explicit forms. So we propose Markov chain Monte Carlo (MCMC) techniques to generate samples from the posterior distributions and in turn computing the Bayes estimators. The approximate Bayes estimators obtained under the assumptions of non-informative priors are compared with the maximum likelihood estimators using Monte Carlo simulations. A numerical example is also presented for illustrative purposes. |
| Databáze: | OpenAIRE |
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