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.

EB00000004428K

Available

• The DD G -classifier in the functional setting