The $$\hbox {DD}^G$$ DD G -classifier in the functional setting
J. A. Cuesta-Albertos (),
M. Febrero-Bande () and
M. Oviedo de la Fuente ()
Additional contact information
J. A. Cuesta-Albertos: University of Cantabria
M. Febrero-Bande: University of Santiago de Compostela
M. Oviedo de la Fuente: University of Santiago de Compostela
TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 1, No 6, 119-142
Abstract:
Abstract The maximum depth classifier was the first attempt to use data depths instead of multivariate raw data in classification problems. Recently, the DD-classifier has addressed some of the serious limitations of this classifier but issues still remain. This paper aims to extend the DD-classifier as follows: first, by enabling it to handle more than two groups; second, by applying regular classification methods (such as kNN, linear or quadratic classifiers, recursive partitioning, etc) to DD-plots, which is particularly useful, because it gives insights based on the diagnostics of these methods; and third, by integrating various sources of information (data depths, multivariate functional data, etc) in the classification procedure in a unified way. This paper also proposes an enhanced revision of several functional data depths and it provides a simulation study and applications to some real data sets.
Keywords: DD-classifier; Functional depths; Functional data analysis; 62-09; 62G99; 62H30 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11749-016-0502-6
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