On the limiting empirical measure of eigenvalues of the sum of rank one matrices with log-concave distribution

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Small Ball Probability, Inverse Theorems, and Applications (2012, background): “For applications in Random Matrix Theory (such as the establishment of the Circular Law) and many related results, we refer to [59, 65, 58, 18, 40, 2, 47] and the references therein.”
On the Marchenko-Pastur and Circular Laws for some Classes of Random Matrices with Dependent Entries (2011, methodology): “Marchenko-Pastur Law Recall that the spectral measure of an n × n symmetric matrix H is a probability measure on the real line defined as LH = 1 n n ∑ i=1 δλi , where λ1 ≤ . . .≤ λn are eigenvalues of A and δx stands for the Dirac mass at x .”