Binary trees for dissimilarity data

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.

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Year of publication:	2010
Authors:	Piccarreta, Raffaella
Published in:	Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 54.2010, 6, p. 1516-1524
Publisher:	Elsevier
Keywords:	Dissimilarity matrix Classification and regression trees Binary segmentation Multivariate responses Perception data Ecological data

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

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