  The distributions of the smallest and largest eigenvalues for the matrix
product $Z^\dagger Z$, where $Z$ is an $n \times m$ complex Gaussian matrix
with correlations both along rows and down columns, are expressed as $m \times
m$ determinants. In the case of correlation along rows, these expressions are
computationally more efficient than those involving sums over partitions and
Schur polynomials reported recently for the same distributions.
