A general averaging method for count data with overdispersion and/or excess zeros in biomedicine

With the aim of providing better estimation for count data with overdispersion and/or excess zeros, we develop a novel estimation method-optimal weighting based on cross-validation-for the zero-inflated negative binomial model, where the Poisson, negative binomial, and zero-inflated Poisson models are all included as its special cases. To facilitate the selection of the optimal weight vector, a K -fold cross-validation technique is adopted. Unlike the jackknife model averaging discussed in Hansen and Racine (2012), the proposed method deletes one group of observations rather than only one observation to enhance the computational efficiency. Furthermore, we also theoretically prove the asymptotic optimality of the newly developed optimal weighting based on cross-validation method. Simulation studies and three empirical applications indicate the superiority of the presented optimal weighting based on cross-validation method when compared with the three commonly used information-based model selection methods and their model averaging counterparts.

Automated summary

To improve estimation for count data with overdispersion and/or excess zeros, a novel estimation method called optimal weighting based on cross-validation is developed for the zero-inflated negative binomial model. A K-fold cross-validation technique is used to select the optimal weight vector. The proposed method enhances computational efficiency by deleting one group of observations, unlike the jackknife model averaging method. The newly developed method is proven to be asymptotically optimal. Simulation studies and empirical applications show that it outperforms three commonly used information-based model selection methods and their model averaging counterparts.