Small ball probability for multiple singular values of symmetric random matrices
Yi Han

TL;DR
This paper studies the probability that a symmetric random matrix has multiple eigenvalues near fixed points, providing bounds that suggest approximate independence and applications to eigenvalue spacing.
Contribution
It establishes bounds on joint small ball probabilities for multiple eigenvalues of symmetric random matrices, extending to multiple locations and applications to eigenvalue relations.
Findings
Joint small ball probability bounds for eigenvalues near fixed points
Approximate factorization indicating quantitative independence
Application to eigenvalue spacing and linear relations
Abstract
Let be an random symmetric matrix with i.i.d. mean , variance 1, following a subGaussian distribution and diagonal elements i.i.d. following a subGaussian distribution with a fixed variance. We investigate the joint small ball probability that has eigenvalues near two fixed locations and , where and are sufficiently separated and in the bulk of the semicircle law. More precisely we prove that for a wide class of entry distributions of that involve all Gaussian convolutions (where denotes the least singular value of a square matrix), The given estimate approximately factorizes as the product of the estimates for the…
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Taxonomy
TopicsRandom Matrices and Applications · Point processes and geometric inequalities · Probability and Risk Models
