Model Regresi Bivariate Zero-Inflated Poisson Pada Kematian Ibu dan Bayi

Abstract

Overdispersion is a condition with greater variance than the mean. One of the causes overdispersion is more zero-value observations so the Zero-Inflated Poisson (ZIP) regression model can be used. As for modeling a pair of discrete data is correlated and overdispersion, it can be used the Bivariate Zero-Inflated Poisson (BZIP) regression model. The BZIP regression model is a model with response variables with mixed distributions between Bivariate Poisson distribution and a point probability at (0,0). Parameters of the BZIP regression model are estimated using maximum likelihood estimation (MLE) with expectation maximization (EM) algorithm. This research was applied to data on number of maternal and infant mortality in the city of Makassar in 2017. The result obtained is the AIC value of the BZIP regression model is 170.976 smaller than the Bivariate Poisson regression model is 198.120. This shows that the BZIP regression model is better used for data with overdispersion.