Model-Based Recursive Partitioning

• Published in: Journal of computational and graphical statistics Vol. 17; no. 2; pp. 492 - 514
• Main Authors: Zeileis, Achim, Hothorn, Torsten, Hornik, Kurt
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 01.06.2008; American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America; Taylor & Francis Ltd
• Subjects: Algorithms; Change points; Datasets; Economic fluctuations; Economic models; Linear regression; Machine learning; Mathematical independent variables; Maximum likelihood; Maximum likelihood method; Modeling; Objective functions; Parameter estimation; Parameter instability; Parametric models; Regression analysis; Studies
• ISSN: 1061-8600; 1537-2715
• DOI: 10.1198/106186008X319331

Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a dataset; (2) test for parameter instability over a set of partitioning variables; (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability; (4) repeat the procedure in each of the daughter nodes. The algorithm yields a partitioned (or segmented) parametric model that can be effectively visualized and that subject-matter scientists are used to analyzing and interpreting.