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Depth-based classification of directional data
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DOI：10.1016/j.eswa.2020.114433.png)
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Abstract
En 中文
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.
Keywords:
Spherical random variables
Directional distance
Angular data depth
Misclassification rate
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