Analysis of Zero Inflated Poisson Regression Model with A New Modified Bias Estimator on Tetanus Neonatorum Data in Bandar Lampung

In Poisson Regression, assuming that the variant value must be equal to the mean (equidispersion), then another alternative is used for data that has a variant value greater than the mean (overdispersion). Overdispersion in Poisson regression can be overcome by using the Zero Inflated Poisson model. The purpose of this study is to model Zero Inflated Poisson (ZIP) with New Modified Bias Estimator in cases of Tetanus Neonatorum in Bandar Lampung, Indonesia. In addition, this study also compares the performance of Zero Inflated Poisson with new modified bias estimator with Maximum Likelihood Estimation (MLE) method in handling multicollinearity and overdispersion in application data. The best model based on the smallest Mean Squared Error (MSE) value. The results of the study indicate that the Zero Inflated Poisson model with a new modified bias estimator is better in modeling Tetanus Neonatorum case data in Bandar Lampung, Indonesia because it has a smaller MSE value (0.8819) than the MLE (75.5678). The Zero Inflated Poisson model with a new modified bias estimator shows that Tetanus Neonatorum in Bandar Lampung, Indonesia is influenced by the percentage of pregnant women examined () and the percentage of neonatal examination ().