Frequentist model averaging for zero-inflated Poisson regression models

This paper considers frequentist model averaging for estimating the unknown parameters of the zero-inflated Poisson regression model. Our proposed weight choice procedure is based on the minimization of an unbiased estimator of a conditional quadratic loss function. We prove that the resulting model average estimator enjoys optimal asymptotic property and improves finite sample properties over the two commonly used information-based model selection estimators and their model average estimators via simulation studies. The proposed method is illustrated by a real data example.