Model selection for zero-inflated regression with missing covariates
Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find out whether the zero-inflation exists or not and how to select the most suitable model. However, the classic AIC criterion for model selection is invalid when the observations are missing. In this paper, we develop a new model selection criterion in line with AIC for the zero-inflated regression models with missing covariates. This method is a modified version of Monte Carlo EM algorithm which is based on the data augmentation scheme. One of the main attractions of this new method is that it is applicable for comparison of candidate models regardless of whether there are missing data or not. What is more, it is very simple to compute as it is just a by-product of Monte Carlo EM algorithm when the estimations of parameters are obtained. A simulation study and a real example are used to illustrate the proposed methodologies.
model selection zero-inflation aic em algorithm missing data