Generalized Linear Models [chapter]

An Introduction to Categorical Data Analysis  
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
more » ... meter 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 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. Model-based recursive partitioning (Zeileis et al. 2008) looks for a piece-wise (or segmented) parametric model M B (Y, {ϑ b }), b = 1, . . . , B that may fit the data set at hand better than a Copyright
doi:10.1002/9780470114759.ch3 fatcat:nw2q4spfg5g5djhrc5vyszz2qm