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http://acervodigital.unesp.br/handle/11449/75788
- Title:
- A bayesian analysis for the parameters of the exponential-logarithmic distribution
- Universidade Estadual Paulista (UNESP)
- 0898-2112
- 1532-4222
- The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.
- 1-Jul-2013
- Quality Engineering, v. 25, n. 3, p. 282-291, 2013.
- 282-291
- Bayesian
- exponential-logarithmic distribution
- Jeffreys
- MCMC
- noninformative prior
- posterior
- Non-informative prior
- Maximum likelihood estimation
- Bayesian networks
- http://dx.doi.org/10.1080/08982112.2013.764431
- Acesso restrito
- outro
- http://repositorio.unesp.br/handle/11449/75788
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