  We consider the problem of estimating covariance and precision matrices, and
their associated discriminant coefficients, from normal data when the rank of
the covariance matrix is strictly smaller than its dimension and the available
sample size. Using unbiased risk estimation, we construct novel estimators by
minimizing upper bounds on the difference in risk over several classes. Our
proposal estimates are empirically demonstrated to offer substantial
improvement over classical approaches.
