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Title: Depth-based classification of directional data Authors:  Giuseppe Pandolfo - University of Naples Federico II (Italy) [presenting]
Antonio D Ambrosio - University of Naples Federico II (Italy)
Abstract: 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 Tukeys, 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 datasets.