Model-based recursive partitioning

Zeileis, Achim and Hothorn, Torsten and Hornik, Kurt ORCID: (2005) Model-based recursive partitioning. Research Report Series / Department of Statistics and Mathematics, 19. Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, Vienna.


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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 data set, (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 effectively be visualized and that subject-matter scientists are used to analyze and interpret.

Item Type: Paper
Keywords: change points / maximum likelihood / parameter instability / recursive partitioning
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 09 Aug 2005 19:40
Last Modified: 24 Oct 2019 13:41


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