BAYESIAN ANALYSIS OF GENERALIZED MODIFIED WEIBULL DISTRIBUTION
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Abstract
Recently, Carrasco et al. (2008) proposed the modied generalized Weibull distribution (GMW). This distribution presents hazard function with bathtub,
unimodal and monotonic shapes. Besides, GMW includes the Weibull, extreme value, exponentiated Weibull and generalized Rayleigh distributions as special cases. In this paper we consider a complete Bayesian analysis assuming noninformative priors for the unknown parameters and the Bayesian analysis is compared to the maximum likelihood approach. We introduce the MCMC algorithms to generate samples from the posterior distributions in order to evaluate the estimators and intervals. The numerical illustration is based on simulated and two lifetime data to illustrate the proposed methodology.
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