  We propose a two-sample test for the means of high-dimensional data when the
data dimension is much larger than the sample size. Hotelling's classical $T^2$
test does not work for this "large $p$, small $n$" situation. The proposed test
does not require explicit conditions in the relationship between the data
dimension and sample size. This offers much flexibility in analyzing
high-dimensional data. An application of the proposed test is in testing
significance for sets of genes which we demonstrate in an empirical study on a
leukemia data set.
