  Signal identification in large-dimensional settings is a challenging problem
in biostatistics. Recently, the method of higher criticism (HC) was shown to be
an effective means for determining appropriate decision thresholds. Here, we
study HC from a false discovery rate (FDR) perspective. We show that the HC
threshold may be viewed as an approximation to a natural class boundary (CB) in
two-class discriminant analysis which in turn is expressible as FDR threshold.
We demonstrate that in a rare-weak setting in the region of the phase space
where signal identification is possible both thresholds are practicably
indistinguishable, and thus HC thresholding is identical to using a simple
local FDR cutoff. The relationship of the HC and CB thresholds and their
properties are investigated both analytically and by simulations, and are
further compared by application to four cancer gene expression data sets.
