On the optimality of the max-depth and max-rank classifiers for spherical data.

DOI10.21136/AM.2020.0331-19MaRDI QIDQ778569

Giovanni C. Porzio, Houyem Demni, Amor Messaoud, Ondřej Vencálek

Publication date: 2 July 2020

Published in: Applications of Mathematics (Search for Journal in Brave)

zbMATH Keywords

von Mises-Fisher distributiondirectional datacosine depthdepth-based classifier

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Order statistics; empirical distribution functions (62G30)

Related Items (1)

Distance-based directional depth classifiers: a robustness study

Uses Software

• ISLR

Cites Work

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• On a notion of data depth based on random simplices
• Ordering directional data: Concepts of data depth on circles and spheres
• On finite-sample robustness of directional location estimators
• A new concept of quantiles for directional data and the angular Mahalanobis depth
• Multivariate and functional classification using depth and distance
• Measures of centrality for multivariate and directional distributions
• A family of distributions on the m-sphere and some hypothesis tests
• An Introduction to Statistical Learning
• Distance‐based depths for directional data
• On maximum depth classifiers: depth distribution approach
• Optimal Rank-Based Tests for the Location Parameter of a Rotationally Symmetric Distribution on the Hypersphere
• On Maximum Depth and Related Classifiers

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