BAYESIAN ANALYSIS OF GENERALIZED MODIFIED WEIBULL DISTRIBUTION
Conteúdo do artigo principal
Resumo
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.
Detalhes do artigo
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).