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#random matrix theory

6 results
math.PR2019

Distribution of Eigenvalues of Random Real Symmetric Block Matrices

Keller Blackwell, Neelima Borade, Charles Devlin VI +5

The paper constructs ensembles of random real symmetric block matrices built from palindromic Toeplitz and general real symmetric matrices, and proves that their eigenvalue distrib…

#random matrix theory#eigenvalue distribution#block matrices#spectral measures
math.PR2019

High-dimensional central limit theorems for eigenvalue distributions of generalized Wishart processes

Jian Song, Jianfeng Yao, Wangjun Yuan

The paper proves central limit theorems describing how the empirical eigenvalue distributions of high‑dimensional generalized Wishart processes fluctuate around their deterministic…

#random matrix theory#wishart processes#eigenvalue fluctuations#central limit theorem
math.PR2019

Large deviations for the largest eigenvalue of rank one deformations of Gaussian ensembles

Mylène Maïda

The paper proves a large deviation principle for the largest eigenvalue of a rank‑one perturbed Gaussian random matrix (GUE or GOE) and shows how, when the perturbation is strong e…

#large deviations#random matrix theory#eigenvalue statistics#rank-one perturbations
math.PR2019

Universal behavior of the corners of Orbital Beta Processes

Cesar Cuenca

The paper studies the eigenvalue distributions of principal submatrices in unitarily‑invariant random matrix ensembles (orbital beta processes) and shows that, after rescaling, the…

#random matrix theory#orbital beta processes#eigenvalue universality#Gaussian beta corners
math.PR2019

Beta Laguerre ensembles in global regime

Hoang Dung Trinh, Khanh Duy Trinh

The paper investigates how the eigenvalue distribution of Beta Laguerre ensembles behaves when the parameter β changes with matrix size, showing convergence to the Marchenko‑Pastur…

#random matrix theory#beta ensembles#Laguerre ensembles#asymptotic analysis
math.ST2019

Normal Approximation and Confidence Region of Singular Subspaces

Dong Xia

The paper develops non‑asymptotic normal approximations for the distance between empirical and true singular subspaces of a noisy matrix, providing explicit formulas, bias correcti…

#singular value decomposition#random matrix theory#non-asymptotic analysis#spectral perturbation