  This article concerns tests for the two-sample location problem when the
dimension is larger than the sample size. The traditional
multivariate-rank-based procedures cannot be used in high dimensional settings
because the sample scatter matrix is not available. We propose a novel
high-dimensional spatial rank test in this article. The asymptotic normality is
established. We can allow the dimension being almost the exponential rate of
the sample sizes. Simulations demonstrate that it is very robust and efficient
in a wide range of distributions.
