Regression Tree for Data with Excess Zeros Count

Abstract

A regression tree method for count data with excess zeros is proposed. At each node, a Poisson regression model which accommodates excess zeros is fitted. A likelihood-base procedure is proposed to select split variables and split sets. Node deviance is then used in the tree pruning process to avoid overfitting. Our method is free of variable selection bias. It is demonstrated to be effective in simulation and real data studies.