Depth-based classification of directional data - Pandolfo, Giuseppe; D’Ambrosio, Antonio (Expert systems with applications, 2021)
A non-parametric procedure based on the concept angular depth function is developed for dealing with classification problems of objects in directional statistics. Several notions of depth for directional data are adopted: the angular simplicial, the angular Tukey’s, the arc distance, the cosine distance and the chord distance depths. The proposed method is flexible and can be applied even in high-dimensional cases when a suitable notion of depth is adopted. Performances are investigated and compared by applying methods to different distributional settings through simulated and real data sets. •A non-parametric spherical-distance-based classifier is proposed.•It is an alternative to the existing depth-based algorithms.•Performances are investigated and compared through simulated and real data sets.