Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
Generic weighted filtering of stochastic signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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This paper analyzes the asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices. Rather than considering traditional large sample-size asymptotics, in this paper the focus is on limited sample size situations, whereby the number of available observations is comparable in magnitude to the observation dimension. Using tools from random matrix theory, the asymptotic behavior of the traditional sample estimates is investigated under the assumption that both the sample size and the observation dimension tend to infinity, while their quotient converges to a positive quantity. Assuming that an asymptotic eigenvalue splitting condition is fulfilled, closed form asymptotic expressions of these estimators are derived, proving inconsistency of the traditional sample estimators in these asymptotic conditions. The derived expressions are shown to provide a valuable insight into the behavior of the sample estimators in the small sample size regime.