On some classifiers based on multivariate ranks

Olusola Makinde*, Biman Chakraborty

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Early online date17 Aug 2017
Publication statusE-pub ahead of print - 17 Aug 2017


  • Error rates
  • non parametric classifiers
  • rank regions
  • rank-based procedures

ASJC Scopus subject areas

  • Statistics and Probability


Dive into the research topics of 'On some classifiers based on multivariate ranks'. Together they form a unique fingerprint.

Cite this