A bayesian analysis for the parameters of the exponential-logarithmic distribution - Moala, Fernando A. [UNESP]; Garcia, Lívia M. [UNESP] (Taylor & Francis, 2013)
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.
Keywords: Bayesian; exponential-logarithmic distribution; Jeffreys; MCMC; noninformative prior; posterior; Non-informative prior; Maximum likelihood estimation; Bayesian networks