Multivariate data analysis and modeling through classification and regression trees

Abstract

This paper provides a multivariate approach to binary segmentation in order to deal with more response variables. Splitting criteria are proposed to grow decision trees with multivariate classification/prediction. These are derived as extensions of criteria used in two-stage binary segmentation. The proposed methodology can be fruitfully performed not only to define decision rules for new cases but also to explore dependency in multivariate data. The feasibility of the method and the interpretation of the final decision trees are discussed in a practical example using a survey of the Bank of Italy.