Extreme eigenvalue statistics of \(m\)-dependent heavy-tailed matrices

DOI10.1214/21-AIHP1152zbMath1491.60008arXiv1910.08511OpenAlexW3215847166MaRDI QIDQ2077358

Yanyan Li

Publication date: 25 February 2022

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.08511

zbMATH Keywords

regular variationlargest eigenvaluesample covariance matrixWigner matrixmarked Poisson processPoisson cluster processdependent random matricesheavy-tailed random matrices

Mathematics Subject Classification ID

Random matrices (probabilistic aspects) (60B20) Stationary stochastic processes (60G10) Extreme value theory; extremal stochastic processes (60G70) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (7)

Covariance kernel of linear spectral statistics for half-heavy tailed Wigner matrices ⋮ Large sample autocovariance matrices of linear processes with heavy tails ⋮ Thin-shell theory for rotationally invariant random simplices ⋮ Large sample correlation matrices: a comparison theorem and its applications ⋮ Spectrum of heavy-tailed elliptic random matrices ⋮ The asymptotic distribution of the condition number for random circulant matrices ⋮ Limiting distributions for eigenvalues of sample correlation matrices from heavy-tailed populations

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