Binary trees for dissimilarity data
AbstractBinary 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 54 (2010)
Issue (Month): 6 (June)
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Web page: http://www.elsevier.com/locate/csda
Dissimilarity matrix Classification and regression trees Binary segmentation Multivariate responses Perception data Ecological data;
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