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arXiv · 2602.19648

Local depth-based classification of directional data

Giuseppe Gismondi·Rebecca Rivieccio·Giuseppe Pandolfo
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Abstract

Directional data arise in many applications where observations are naturally represented as unit vectors or as observations on the surface of a unit hypersphere. In this context, statistical depth functions provide a center--outward ordering of the data. This work aims at proposing the use of a local notion of data depth function to be applied in the DD-plot (Depth vs. Depth plot) to classify directional data. The proposed method is investigated through an extensive simulation study and two real-data examples.

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Giuseppe Gismondi, Rebecca Rivieccio, Giuseppe Pandolfo. 2026-02-23. Local depth-based classification of directional data. https://arxiv.org/abs/2602.19648

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