Untitled - UFRJ

Bayesian Measures of Model Complexity and Fit inAsymmetrical ScenariosCléber da Costa FigueiredoESPM-SP/EESP-FGVMônica Carneiro SandovalUniversity of São PauloHeleno BolfarineUniversity of São PauloWith the enthusiasm brought from the last years in finding better fits for asymmetrical data set,we present Bayesian approaches for measuring the model complexity and fit in asymmetrical scenarios.Model fitting is implemented by proposing the asymmetric deviance information criterion, ADIC, amodification of the ordinary DIC, and the Evidence Deviance Information Criterium, EDIC, anothermodification. For selling our proposal, we made an extension of a study of Figueiredo et al. (2008)that utilized the asymmetrical methodology, but did not extend it for a very large class of asymmetricaldistributions. As well, we need to utilize a numerical optimization for measure the complexity and thefit of the model, since we are using not symmetrical approaches for doing it. Whatever similarity (in thetitle of this paper) with the Spiegelhalter et al. (2002) work is not a mere coincidence.83