Binary trees for dissimilarity data

Authors:
Raffaella Piccarreta
Affiliations:
Department of Decision Sciences, Bocconi University, Milan, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 6
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Abstract

Binary segmentation procedures (in particular, classification and regression trees) are extended to study the relation between dissimilarity data and a set of explanatory variables. The proposed split criterion is very flexible, and can be applied to a wide range of data (e.g., mixed types of multiple responses, longitudinal data, sequence data). Also, it can be shown to be an extension of well-established criteria introduced in the literature on binary trees.