In this paper, we study large Euclidean random matrices Mn=(fn(‖xi−xj‖2))n×n where xi’s are i.i.d. points lp-norm uniformly distributed over the N dimensional lp ellipsoid or its surface, fn is a real function on [0,∞) and ‖⋅‖ is the Euclidean distance. Under the assumption that...

This paper revisits the methodology of Stein (1975, 1986) for estimating a covariance matrix in the setting where the number of variables can be of the same magnitude as the sample size. Stein proposed to keep the eigenvectors of the sample covariance matrix but to shrink the eigenvalues. By...

This paper introduces a new method for deriving covariance matrix estimators that are decision-theoretically optimal. The key is to employ large-dimensional asymptotics: the matrix dimension and the sample size go to infinity together, with their ratio converging to a finite, nonzero limit. As...