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We introduce squared families, which are families of probability densities obtained by squaring a linear transformation of a statistic. Squared families are singular, however their singularity can easily be handled so that they form regular…

Machine Learning · Statistics 2025-03-28 Russell Tsuchida , Jiawei Liu , Cheng Soon Ong , Dino Sejdinovic

We investigate the fluctuations around the average density profile in the weakly asymmetric exclusion process with open boundaries in the steady state. We show that these fluctuations are given, in the macroscopic limit, by a centered…

Other Condensed Matter · Physics 2009-11-11 B. Derrida , C. Enaud , C. Landim , S. Olla

We analyze the asymptotic fluctuations of linear eigenvalue statistics of random centrosymmetric matrices with i.i.d. entries. We prove that for a complex analytic test function, the centered and normalized linear eigenvalue statistics of…

Probability · Mathematics 2025-10-20 Indrajit Jana , Sunita Rani

We study uniformly random lozenge tilings of general simply connected polygons. Under a technical assumption that is presumably generic with respect to polygon shapes, we show that the local statistics around a cusp point of the arctic…

Probability · Mathematics 2024-12-18 Jiaoyang Huang , Fan Yang , Lingfu Zhang

We consider rectangular random matrices of size $p\times n$ belonging to the real Wishart-Laguerre ensemble also known as the chiral Gaussian orthogonal ensemble. This ensemble appears in many applications like QCD, mesoscopic physics, and…

Mathematical Physics · Physics 2015-09-17 Tim Wirtz , Gernot Akemann , Thomas Guhr , Mario Kieburg , René Wegner

We study the adjacency matrix of the Linial-Meshulam complex model, which is a higher-dimensional generalization of the Erd\H{o}s-R\'enyi graph model. Recently, Knowles and Rosenthal proved that the empirical spectral distribution of the…

Probability · Mathematics 2023-08-23 Shu Kanazawa , Khanh Duy Trinh

We consider settings where the observations are drawn from a zero-mean multivariate (real or complex) normal distribution with the population covariance matrix having eigenvalues of arbitrary multiplicity. We assume that the eigenvectors of…

Statistics Theory · Mathematics 2009-01-22 N. Raj Rao , James A. Mingo , Roland Speicher , Alan Edelman

We consider the ensemble of adjacency matrices of Erd\H{o}s-R\'{e}nyi random graphs, that is, graphs on $N$ vertices where every edge is chosen independently and with probability $p\equiv p(N)$. We rescale the matrix so that its bulk…

Probability · Mathematics 2013-07-12 László Erdős , Antti Knowles , Horng-Tzer Yau , Jun Yin

We consider $n\times n$ random matrices $M_{n}=\sum_{\alpha =1}^{m}{\tau _{\alpha }}\mathbf{y}_{\alpha }\otimes \mathbf{y}_{\alpha }$, where $\tau _{\alpha }\in \mathbb{R}$, $\{\mathbf{y}_{\alpha }\}_{\alpha =1}^{m}$ are i.i.d. isotropic…

Probability · Mathematics 2013-12-02 O. Guédon , A. Lytova , A. Pajor , L. Pastur

This manuscript studies statistical properties of linear classifiers obtained through minimization of an unregularized convex risk over a finite sample. Although the results are explicitly finite-dimensional, inputs may be passed through…

Machine Learning · Computer Science 2012-06-15 Matus Telgarsky

Symmetric matrices with zero row sums occur in many theoretical settings and in real-life applications. When the offdiagonal elements of such matrices are i.i.d. random variables and the matrices are large, the eigenvalue distributions…

Disordered Systems and Neural Networks · Physics 2023-06-27 Pawat Akara-pipattana , Oleg Evnin

The asymptotic normality for a large family of eigenvalue statistics of a general sample covariance matrix is derived under the ultra-high dimensional setting, that is, when the dimension to sample size ratio $p/n \to \infty$. Based on this…

Methodology · Statistics 2021-09-15 Jiaxin Qiu , Zeng Li , Jianfeng Yao

In this article, we revisit the question of fluctuations of linear statistics of beta ensembles in the single cut and non-critical regime for general potentials $V$ under mild regularity and growth assumptions. Our main objective is to…

Probability · Mathematics 2024-03-27 Jürgen Angst , Ronan Herry , Dominique Malicet , Guillaume Poly

The local eigenvalue statistics of large random matrices near a hard edge transitioning into a soft edge are described by the Bessel process associated with a large parameter $\alpha$. For this point process, we obtain 1) exponential moment…

Probability · Mathematics 2021-04-26 Christophe Charlier , Jonatan Lenells

In this paper we find spectral properties in the large $N$ limit of Dirac operators that come from random finite noncommutative geometries. In particular for a Gaussian potential the limiting eigenvalue spectrum is shown to be universal…

High Energy Physics - Theory · Physics 2022-06-10 Masoud Khalkhali , Nathan Pagliaroli

We present a generalization of the method of the local relaxation flow to establish the universality of local spectral statistics of a broad class of large random matrices. We show that the local distribution of the eigenvalues coincides…

Mathematical Physics · Physics 2010-08-20 Laszlo Erdos , Benjamin Schlein , Horng-Tzer Yau , Jun Yin

For large random matrices $X$ with independent, centered entries but not necessarily identical variances, the eigenvalue density of $XX^*$ is well-approximated by a deterministic measure on $\mathbb{R}$. We show that the density of this…

Probability · Mathematics 2017-11-22 Johannes Alt

We analyze properties of non-hermitian matrices of size M constructed as square submatrices of unitary (orthogonal) random matrices of size N>M, distributed according to the Haar measure. In this way we define ensembles of random matrices…

chao-dyn · Physics 2009-10-31 Karol Zyczkowski , Hans-Juergen Sommers

Basing on our recent results on the $1/n$-expansion in unitary invariant random matrix ensembles, known as matrix models, we prove that the local eigenvalue statistic, arising in a certain neighborhood of the edges of the support of the…

Mathematical Physics · Physics 2007-05-23 L. Pastur , M. Shcherbina

Vinberg cones and the ambient vector spaces are important in modern statistics of sparse models and of graphical models. The aim of this paper is to study eigenvalue distributions of Gaussian, Wigner and covariance matrices related to…

Statistics Theory · Mathematics 2020-09-02 Hideto Nakashima , Piotr Graczyk