A procedure to detect general association based on concentration of ranks.

In modern high-throughput applications, it is important to identify pairwise associations between variables, and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a new non-parametric association test of association between two variables that measures the concentration of paired ranked points. Here 'concentration' is quantified using a disk-covering statistic similar to those employed in spatial data analysis. Considerations from the theory of Boolean coverage processes provide motivation, as well as an R2-like quantity to summarize strength of association. Analysis of simulated and real datasets demonstrate that the method is robust and often powerful in comparison to competing general association tests.