On rank distribution classifiers for high-dimensional data

Olusola Samuel Makinde

Journal of Applied Statistics, 2020, vol. 47, issue 13-15, 2895-2911

Abstract: Spatial sign and rank-based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high-dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2895-2911

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DOI: 10.1080/02664763.2020.1768227

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