RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks

Ondrej Vencalek () and Olusola Samuel Makinde ()
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Ondrej Vencalek: Palacky University
Olusola Samuel Makinde: Federal University of Technology

AStA Advances in Statistical Analysis, 2021, vol. 105, issue 4, No 7, 675-693

Abstract: Abstract Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.

Keywords: Data depth; Rank; Nonparametric classifier; Rank-rank plot; Error rates (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10182-021-00423-7

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