DDα-classification of asymmetric and fat-tailed data

Abstract

The DDα-procedure is a fast nonparametric method for supervised classification of d-dimensional objects into q ≥ 2 classes. It is based on q-dimensional depth plots and the α-procedure, which is an efficient algorithm for discrimination in the depth space [0, 1]q. Specifically, we use two depth functions that are well computable in high dimensions, the zonoid depth and the random Tukey depth, and compare their performance for different simulated data sets, in particular asymmetric elliptically and t-distributed data.