Local depth-based classification of directional data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914344017592320 |
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| author | Gismondi, Giuseppe Rivieccio, Rebecca Pandolfo, Giuseppe |
| author_facet | Gismondi, Giuseppe Rivieccio, Rebecca Pandolfo, Giuseppe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19648 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Local depth-based classification of directional data Gismondi, Giuseppe Rivieccio, Rebecca Pandolfo, Giuseppe Methodology 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. |
| title | Local depth-based classification of directional data |
| topic | Methodology |
| url | https://arxiv.org/abs/2602.19648 |