A bayesian analysis for the parameters of the exponential-logarithmic distribution

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Data

2013-07-01

Autores

Moala, Fernando A. [UNESP]
Garcia, Lívia M. [UNESP]

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Resumo

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.

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Bayesian, exponential-logarithmic distribution, Jeffreys, MCMC, noninformative prior, posterior, Non-informative prior, Maximum likelihood estimation, Bayesian networks

Como citar

Quality Engineering, v. 25, n. 3, p. 282-291, 2013.