Zero Inflated Binomial Model for Infant Mortality Data in Indonesia

摘要

This paper discusses overdispersed binomial models applied to infant mortality data in Indonesia. Overdispersion usually occurs when the data has many zeros, or called as excess zeros. In such cases, binomial models are less fit and the type I error can be inflated or higher false positive rates can be obtained. This problem can be resolved by using zero inflated binomial (ZIB) models. Hall (2000) applied ZIB models by modifying the zero inflated Poisson (ZIP) models developed by Lambert (1992). In the ZIB models, the response variable was assumed to be distributed as a mixture of non-zero value distribution consisted of binomial (n,π) and a distribution of the binary zero-indicator. It was also assumed that the mixing probability was p. The fitness of the model was assessed using ROC curves as well as other criteria such as AIC, AICC, and BIC. The result showed that ZIB model has better fit in terms of overcoming the overdipersed binomial data.