Model-based recursive partitioning

Achim Zeileis, Torsten Hothorn, Kurt Hornik

Publication: Working/Discussion PaperWU Working Paper

115 Downloads (Pure)

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
Place of PublicationVienna
PublisherInstitut für Statistik und Mathematik, WU Vienna University of Economics and Business
DOIs
Publication statusPublished - 2005

Publication series

SeriesResearch Report Series / Department of Statistics and Mathematics
Number19

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

Cite this