Count data frequently appear in many scientific studies. In this article, we propose a regression tree method called CORE for analyzing such data. At each node, besides a Poisson regression, a count regression such as hurdle, negative binomial, or zero-inflated regression which can accommodate over-dispersion and/or excess zeros is fitted. A likelihood-based procedure is suggested to select split variables and split sets. Node deviance is then used in the tree pruning process to avoid overfitting. CORE is able to eliminate variable selection bias. In the simulations and real data studies, we show that CORE has some advantages over the existing method, MOB.

中文翻译：

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计算回归树

计数数据经常出现在许多科学研究中。在本文中，我们提出了一种称为CORE的回归树方法来分析此类数据。在每个节点上，除了泊松回归外，还可以安装计数回归（例如，障碍，负二项式或零膨胀回归），这些回归可以容纳过度分散和/或过多的零。建议采用基于似然的程序来选择拆分变量和拆分集。然后在树修剪过程中使用节点偏差以避免过度拟合。CORE能够消除变量选择偏差。在仿真和实际数据研究中，我们表明CORE与现有方法MOB相比具有一些优势。