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Central limit theorem for mesoscopic eigenvalue statistics of deformed Wigner matrices and sample covariance matrices
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.ORCID iD: 0000-0003-0954-3231
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2021 (English)In: Annales de l'I.H.P. Probabilites et statistiques, ISSN 0246-0203, E-ISSN 1778-7017, Vol. 57, no 1, p. 506-546Article in journal (Refereed) Published
Abstract [en]

We consider N by N deformed Wigner random matrices of the form X-N = H-N + A(N), where H-N is a real symmetric or complex Hermitian Wigner matrix and A(N) is a deterministic real bounded diagonal matrix. We prove a universal Central Limit Theorem for the linear eigenvalue statistics of X-N for all mesoscopic scales both in the spectral bulk and at regular edges where the global eigenvalue density vanishes as a square root. The method relies on studying the characteristic function of the linear statistics (Landon and Sosoe (2018)) by using the cumulant expansion method, along with local laws for the Green function of X-N (Ann. Probab. 48 (2020) 963-1001; Probab. Theory Related Fields 169 (2017) 257-352; J. Math. Phys. 54 (2013) 103504) and analytic subordination properties of the free additive convolution (Dallaporta and Fevrier (2019); Random Matrices Theory Appl. 9 (2020) 2050011). We also prove the analogous results for high-dimensional sample covariance matrices.

Place, publisher, year, edition, pages
Project Euclid , 2021. Vol. 57, no 1, p. 506-546
Keywords [en]
Linear eigenvalue statistics, Deformed Wigner matrices, Sample covariance matrices
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-292959DOI: 10.1214/20-AIHP1086ISI: 000628812400018Scopus ID: 2-s2.0-85104268870OAI: oai:DiVA.org:kth-292959DiVA, id: diva2:1545379
Note

QC 20210419

Available from: 2021-04-19 Created: 2021-04-19 Last updated: 2022-06-25Bibliographically approved

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Li, YitingSchnelli, KevinXu, Yuanyuan

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