Model-based Recursive Partitioning

Achim Zeileis, Torsten Hothorn, Kurt Hornik

Publication: Scientific journalJournal articlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)492 - 514
JournalJournal of Computational and Graphical Statistics
Volume17
Issue number2
Publication statusPublished - 1 Nov 2008

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