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

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...