Count regression trees

Nan-Ting Liu (), Feng-Chang Lin () and Yu-Shan Shih ()
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Nan-Ting Liu: National Chung Cheng University
Feng-Chang Lin: University of North Carolina at Chapel Hill
Yu-Shan Shih: National Chung Cheng University

Advances in Data Analysis and Classification, 2020, vol. 14, issue 1, No 2, 5-27

Abstract: Abstract 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.

Keywords: Hurdle model; GUIDE; MOB; Negative binomial model; Score residual; Zero-inflated model; 62G08; 62J12 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11634-019-00358-7

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