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On the limiting spectral density of random matrices filled with stochastic processes

Matthias Löwe and Kristina Schubert

Abstract

We discuss the limiting spectral density of real symmetric random matrices. In contrast to standard random matrix theory, the upper diagonal entries are not assumed to be independent, but we will fill them with the entries of a stochastic process. Under assumptions on this process which are satisfied, e.g., by stationary Markov chains on finite sets, by stationary Gibbs measures on finite state spaces, or by Gaussian Markov processes, we show that the limiting spectral distribution depends on the way the matrix is filled with the stochastic process. If the filling is in a certain way compatible with the symmetry condition on the matrix, the limiting law of the empirical eigenvalue distribution is the well-known semi-circle law. For other fillings we show that the semi-circle law cannot be the limiting spectral density.

MSC 2010: 60B20; 60F05

Communicated by Werner Kirsch


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Received: 2016-06-02
Accepted: 2019-01-09
Published Online: 2019-03-12
Published in Print: 2019-06-01

© 2019 Walter de Gruyter GmbH, Berlin/Boston