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Use of Poisson Spatio-Temporal Regression Models for theBrazilian Amazon Forest Malaria Fever Counting DataJorge Alberto AchcarFMRP/USP & FCT/UNESPEdson Zangiacomi MartinezFMRP/USPAparecida Doniseti Pires de SouzaFCT/UNESPVilma Mayumi TachibanaFCT/UNESPEdilson Ferreira FloresFCT/UNESPIn this paper, we use Poisson spatial-temporal regression models to analyse a Brazilian AmazonForest malaria fever counting data for the period ranging from 1999 to 2008. In this study, we havethe present of some covariates that could be important in the yearly prediction of MFD (malaria feverdisease), as deforestation rate. We obtain the inferences of interest using a Bayesian approach andMCMC (Markov Chain Monte Carlo) methods to simulate samples for the joint posterior distributionof interest. Discrimination of different models is also discussed.Keywords: Poisson Regression Models, CAR Models, Malaria Fever, Markov Chain Monte Carlo.49
Generalized Exponential Distribution: A Bayesian ApproachUsing MCMC MethodsJorge Alberto AchcarUNESP and FMRP-USPFernando Antônio MoalaDMEC, UNESPJuliana BoletaFMRP-USPThe generalized exponential distribution could be a good alternative to analyse lifetime data, asan alternative for the use of standard existing lifetime distributions as exponential, Weibull or gammadistributions. Assuming different noninformative prior distributions for the parameters of the model,we introduce a Bayesian analysis using MCMC (Markov Chain Monte Carlo) methods. Some numericalillustrations considering simulated and real lifetime data are presented to illustrate the proposedmethodology, especially the effects of different priors on the posterior summaries of interest.50
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