On the choice of splitting rules for model-based trees for ordinal responses

The focus of the contribution is on the splitting criterion for model-based treeprocedure based on the class ofCUBmixture models for the analysis of ordinal scores. Theflexibility of the chosen modelling framework allows to select the splitting criterion to growthe tree according to the purposes of the study and the available data. In particular, theselection of variables yielding to the best partitioning results can be driven by fitting measuresor classical likelihood and deviance measures. The contribution proposes to investigate thefeatures of the available decision rules by a set of Montecarlo experiments, thus implicitlyfacing the problem of selecting the model-based tree to obtain an adequate and satisfyingoverview of response profiles.