On some classifiers based on multivariate ranks

Olusola Makinde and Biman Chakraborty

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 16, 3955-3969

Abstract: Non parametric approaches to classification have gained significant attention in the last two decades. In this paper, we propose a classification methodology based on the multivariate rank functions and show that it is a Bayes rule for spherically symmetric distributions with a location shift. We show that a rank-based classifier is equivalent to optimal Bayes rule under suitable conditions. We also present an affine invariant version of the classifier. To accommodate different covariance structures, we construct a classifier based on the central rank region. Asymptotic properties of these classification methods are studied. We illustrate the performance of our proposed methods in comparison to some other depth-based classifiers using simulated and real data sets.

Date: 2018
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DOI: 10.1080/03610926.2017.1366520

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