Gaining Insight With Recursive Partitioning Of Generalized Linear Models

Rusch, Thomas and Zeileis, Achim (2011) Gaining Insight With Recursive Partitioning Of Generalized Linear Models. Research Report Series / Department of Statistics and Mathematics, 109. WU Vienna University of Economics and Business, Vienna.


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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-linearily 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 influence on a parametric model. This method conducts recursive partitioning of a the 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 methods 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.

Item Type: Paper
Keywords: model-based recursive partitioning / generalized linear models / functional trees / parameter instability / maximum likelihood
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Josef Leydold
Date Deposited: 27 Jun 2011 11:53
Last Modified: 22 Oct 2019 00:41


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