Multivariate functional halfspace depth, with applications to the classification of multivariate curves

First, we describe multivariate functional halfspace depth (MFHD), which has recently been proposed as a new tool to study multivariate functional data. This depth function allows to estimate the central tendency and the variability of multiple sets of curves, and to detect outlying curves. The multivariate sample of curves may include warping functions, derivatives and integrals of the original curves for a better overall representation of the functional data via the depth. Next, we present the application of this depth function to supervised classification. We compare several existing and new classifiers on real and simulated data. In particular, their behavior is studied in the presence of outlying curves.

Reference:
Claeskens, G., Hubert, M., Slaets, L., Vakili, K. (2013).

Multivariate functional halfspace depth. Journal of the American Statistical Association, in press.


contact: anna.paganoni@polimi.it