Some Multivariate Tests of Independence Based on Ranks of Nearest Neighbors

Several parametric and nonparametric tests of independence between two random vectors are available in the literature. But, many of them perform poorly for high-dimensional data and are not applicable when the dimension exceeds the sample size. In this article, we propose some tests based on ranks of nearest neighbors, which can be conveniently used in high dimension, low sample size situations. Several simulated and real datasets are analyzed to show the utility of the proposed tests. Codes for implementation of the proposed tests are available as supplementary materials.