Abstract
Violation of the Poisson regression assumption can cause the model formed will produce an unbiased estimator. There is a good method for estimating parameters on small sample sizes and on all distributions, namely the Bayesian method. The number of death from chronic Filariasis data violates the Poisson regression assumption, so it is modeled with the Bayesian Hurdle Poisson Regression. With the Bayesian method, convergence is fullfilled when 300000 iterations and 7 thin are performed. The results showed that in the logit model only one predictor variable had a significant effect on the number of cases of death due to chronic Filiariasis in 34 Provinces in Indonesia . The Truncated Poisson model resulted in all predictor variables having a significant effect on the number of cases of death due to chronic Filariasis.
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