  We consider settings where the observations are drawn from a zero-mean
multivariate (real or complex) normal distribution with the population
covariance matrix having eigenvalues of arbitrary multiplicity. We assume that
the eigenvectors of the population covariance matrix are unknown and focus on
inferential procedures that are based on the sample eigenvalues alone (i.e.,
"eigen-inference"). Results found in the literature establish the asymptotic
normality of the fluctuation in the trace of powers of the sample covariance
matrix. We develop concrete algorithms for analytically computing the limiting
quantities and the covariance of the fluctuations. We exploit the asymptotic
normality of the trace of powers of the sample covariance matrix to develop
eigenvalue-based procedures for testing and estimation. Specifically, we
formulate a simple test of hypotheses for the population eigenvalues and a
technique for estimating the population eigenvalues in settings where the
cumulative distribution function of the (nonrandom) population eigenvalues has
a staircase structure. Monte Carlo simulations are used to demonstrate the
superiority of the proposed methodologies over classical techniques and the
robustness of the proposed techniques in high-dimensional, (relatively) small
sample size settings. The improved performance results from the fact that the
proposed inference procedures are "global" (in a sense that we describe) and
exploit "global" information thereby overcoming the inherent biases that
cripple classical inference procedures which are "local" and rely on "local"
information.
