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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/75788
Title: 
A bayesian analysis for the parameters of the exponential-logarithmic distribution
Author(s): 
Institution: 
Universidade Estadual Paulista (UNESP)
ISSN: 
  • 0898-2112
  • 1532-4222
Abstract: 
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.
Issue Date: 
1-Jul-2013
Citation: 
Quality Engineering, v. 25, n. 3, p. 282-291, 2013.
Time Duration: 
282-291
Keywords: 
  • Bayesian
  • exponential-logarithmic distribution
  • Jeffreys
  • MCMC
  • noninformative prior
  • posterior
  • Non-informative prior
  • Maximum likelihood estimation
  • Bayesian networks
Source: 
http://dx.doi.org/10.1080/08982112.2013.764431
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/75788
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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