1,365 research outputs found
Asymptotics of random density matrices
We investigate random density matrices obtained by partial tracing larger
random pure states. We show that there is a strong connection between these
random density matrices and the Wishart ensemble of random matrix theory. We
provide asymptotic results on the behavior of the eigenvalues of random density
matrices, including convergence of the empirical spectral measure. We also
study the largest eigenvalue (almost sure convergence and fluctuations)
Cleaning large correlation matrices: tools from random matrix theory
This review covers recent results concerning the estimation of large
covariance matrices using tools from Random Matrix Theory (RMT). We introduce
several RMT methods and analytical techniques, such as the Replica formalism
and Free Probability, with an emphasis on the Marchenko-Pastur equation that
provides information on the resolvent of multiplicatively corrupted noisy
matrices. Special care is devoted to the statistics of the eigenvectors of the
empirical correlation matrix, which turn out to be crucial for many
applications. We show in particular how these results can be used to build
consistent "Rotationally Invariant" estimators (RIE) for large correlation
matrices when there is no prior on the structure of the underlying process. The
last part of this review is dedicated to some real-world applications within
financial markets as a case in point. We establish empirically the efficacy of
the RIE framework, which is found to be superior in this case to all previously
proposed methods. The case of additively (rather than multiplicatively)
corrupted noisy matrices is also dealt with in a special Appendix. Several open
problems and interesting technical developments are discussed throughout the
paper.Comment: 165 pages, article submitted to Physics Report
Detection of an unknown rank-one component in white noise
We consider the detection of an unknown and arbitrary rank-one signal in a spatial sector scanned by a small number of beams. We address the problem of finding the maximal invariant for the problem at hand and show that it consists of the ratio of the eigenvalues of a Wishart matrix to its trace. Next, we derive the generalized-likelihood ratio test (GLRT) along with expressions for its probability density function (pdf) under both hypotheses. Special attention is paid to the case m= 2, where the GLRT is shown to be a uniformly most powerful invariant (UMPI). Numerical simulations attest to the validity of the theoretical analysis and illustrate the detection performance of the GLRT
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