| 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 dataset; (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 be effectively visualized and that subject-matter scientists are used to analyzing and interpreting. |
