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Recursive Partitioning and Tree-based Methods

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Handbook of Computational Statistics

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Abstract

Tree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. The applications of these methods are far reaching. They include financial firms (credit cards Altman 2002; Frydman et al. 2002 and investments Pace 1995; Brennan et al. 2001), manufacturing and marketing companies Levin et al. (1995), and pharmaceutical companies. In the past decades, there have been many applications in genomics and bioinformatics Zhang et al. (2001).

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Acknowledgements

This research is supported in part by grant R01DA016750 from the National Institutes on Drug Abuse.

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Authors and Affiliations

  1. Yale University School of Medicine, New Haven, CT, USA

    Heping Zhang

  2. Sun Yat-Sen University, Guangzhou, China

    Heping Zhang

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  1. Heping Zhang
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Editors and Affiliations

  1. Dept. Computational & Data, Sciences, George Mason University, University Drive 4400, Fairfax, 22030-4444, Virginia, USA

    James E. Gentle

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. Dept. Socioinformation, Okayama University, Ridai-cho 1-1, Okayama, 700-0005, Japan

    Yuichi Mori

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Zhang, H. (2012). Recursive Partitioning and Tree-based Methods. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_29

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