Pseudo-Ranks: How to Calculate Them Efficiently in R

Pseudo-Ranks: How to Calculate Them Efficiently in R

: nonparametric statistics, ranks, pseudo-ranks, R

Many popular nonparametric inferential methods are based on ranks. Among the most
commonly used and most famous tests are for example the Wilcoxon-Mann-Whitney test
for two independent samples, and the Kruskal-Wallis test for multiple independent groups.
However, recently, it has become clear that the use of ranks may lead to paradoxical results
in case of more than two groups. Luckily, these problems can be avoided simply by using
pseudo-ranks instead of ranks. These pseudo-ranks, however, suffer from being (a) at
first less intuitive and not as straightforward in their interpretation, (b) computationally
much more expensive to calculate. The computational cost has been prohibitive, for
example, for large-scale simulative evaluations or application of resampling-based pseudorank procedures. In this paper, we provide different algorithms to calculate pseudo-ranks
efficiently in order to solve problem (b) and thus render it possible to overcome the current
limitations of procedures based on pseudo-ranks.

Martin Happ

https://www.jstatsoft.org/article/view/v095c01

University of Salzburg

October 2020

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