Gaining Insight with Recursive Partitioning of Generalized Linear Models

Rusch, Thomas and Zeileis, Achim (2013) Gaining Insight with Recursive Partitioning of Generalized Linear Models. Journal of Statistical Computation and Simulation, 83 (7). pp. 1301-1315. ISSN 0094-9655


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Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearly give rise to different model parameter values. We present an approach that combines generalized linear models with recursive partitioning that offers enhanced interpretability of classical trees as well as providing an explorative way to assess a candidate variable's in uence on a parametric model. This method conducts recursive partitioning of a generalized linear model by (1) fitting the model to the data set, (2) testing for parameter instability over a set of partitioning variables, (3) splitting the data set with respect to the variable associated with the highest instability. The outcome is a tree where each terminal node is associated with a generalized linear model. We will show the method's versatility and suitability to gain additional insight into the relationship of dependent and independent variables by two examples, modelling voting behaviour and a failure model for debt amortization, and compare it to alternative approaches.

Item Type: Article
Additional Information: To see the final version of this paper please visit the publisher's website. Access to the published version requires a subscription.
Keywords: model-based recursive partitioning / generalized linear models / model trees / functional trees / parameter instability / maximum likelihood
Classification Codes: AMS 62J99 ; 62P25, 62H30
Divisions: Departments > Finance, Accounting and Statistics
Version of the Document: Draft
Variance from Published Version: Minor
Depositing User: ePub Administrator
Date Deposited: 06 May 2013 09:21
Last Modified: 28 Feb 2017 05:54
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