Frequentist model averaging for zero‐inflated Poisson regression models

Abstract

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.