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We study the maximum of the random assignment process on rectangular matrices. We derive first-order asymptotics for the expected maximum, prove a law of large numbers under mild tail assumptions, and obtain exponential upper bounds for the…

Probability · Mathematics 2025-09-23 Timofey Moskalenko

Consider a sample of a centered random vector with unit covariance matrix. We show that under certain regularity assumptions, and up to a natural scaling, the smallest and the largest eigenvalues of the empirical covariance matrix converge,…

Probability · Mathematics 2018-03-16 Djalil Chafaï , Konstantin Tikhomirov

We develop an instanton technique for calculations of correlation functions characterizing statistical behavior of the elastic string in disordered media and apply the proposed approach to correlations of string free energies corresponding…

Soft Condensed Matter · Physics 2009-11-07 Ya. M. Blanter , V. M. Vinokur

For an $n\times n$ Laplacian random matrix $L$ with Gaussian entries it is proven that the fluctuations of the largest eigenvalue and the largest diagonal entry of $L/\sqrt{n-1}$ are Gumbel. We first establish suitable non-asymptotic…

Probability · Mathematics 2021-01-22 Santiago Arenas-Velilla , Victor Pérez-Abreu

The extreme value dependence of regularly varying stationary time series can be described by the spectral tail process. Drees, Segers and Warchol [Extremes 18(3): 369--402, 2015] proposed estimators of the marginal distributions of this…

Statistics Theory · Mathematics 2019-07-23 Holger Drees , Miran Knezevic

We study non-perturbative aspects of the large N duality between Chern-Simons theory and topological strings, and we find a rich structure of large N phase transitions in the complex plane of the 't Hooft parameter. These transitions are…

High Energy Physics - Theory · Physics 2014-11-20 Marcos Marino , Sara Pasquetti , Pavel Putrov

We present a flexible, deterministic numerical method for computing left-tail rare events of sums of non-negative, independent random variables. The method is based on iterative numerical integration of linear convolutions by means of…

Computation · Statistics 2024-05-03 Nadhir Ben Rached , Håkon Hoel , Johannes Vincent Meo

In this paper, a novel approach to the problem of estimating the heavy-tail exponent alpha>0 of a distribution is proposed. It is based on the fact that block-maxima of size m of the independent and identically distributed data scale at a…

Statistics Theory · Mathematics 2007-06-13 Stilian A. Stoev , George Michailidis , Murad S. Taqqu

Consider a Hermitian matrix model under an external potential with spiked external source. When the external source is of rank one, we compute the limiting distribution of the largest eigenvalue for general, regular, analytic potential for…

Mathematical Physics · Physics 2010-12-21 Jinho Baik , Dong Wang

Large deviations for fat tailed distributions, i.e. those that decay slower than exponential, are not only relatively likely, but they also occur in a rather peculiar way where a finite fraction of the whole sample deviation is concentrated…

Statistical Mechanics · Physics 2015-06-03 Mario Filiasi , Giacomo Livan , Matteo Marsili , Maria Peressi , Erik Vesselli , Elia Zarinelli

Consider a random symmetric matrix with i.i.d.~entries on and above its diagonal that are products of Bernoulli random variables and random variables with sub-Gaussian tails. Such a matrix will be called a sparse Wigner matrix and can be…

Probability · Mathematics 2023-04-27 Fanny Augeri , Anirban Basak

We propose an approach to compute the conditional moments of fat-tailed phenomena that, only looking at data, could be mistakenly considered as having infinite mean. This type of problems manifests itself when a random variable Y has a…

Applications · Statistics 2018-08-02 Nassim Nicholas Taleb , Pasquale Cirillo

We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of…

Machine Learning · Computer Science 2021-08-24 Ziwei Ji , Nathan Srebro , Matus Telgarsky

We study instanton contribution to the partition function of the one matrix model in the k-th multicritical region, which corresponds to the (2,2k-1) minimal model coupled to Liouville theory. The instantons in the one matrix model are…

High Energy Physics - Theory · Physics 2009-11-10 Akira Sato , Asato Tsuchiya

Finite sample properties of random covariance-type matrices have been the subject of much research. In this paper we focus on the "lower tail" of such a matrix, and prove that it is subgaussian under a simple fourth moment assumption on the…

Probability · Mathematics 2013-12-11 Roberto Imbuzeiro Oliveira

Consider a $n\times n$ sparse non-Hermitian random matrix $X_n$ defined as the Hadamard product between a random matrix with centered independent and identically distributed entries and a sparse Bernoulli matrix with success probability…

Probability · Mathematics 2026-02-25 Walid Hachem , Michail Louvaris , Jamal Najim

Let $X_k$ denote the number of $k$-term arithmetic progressions in a random subset of $\mathbb{Z}/N\mathbb{Z}$ or $\{1, \dots, N\}$ where every element is included independently with probability $p$. We determine the asymptotics of $\log…

Probability · Mathematics 2019-11-12 Bhaswar B. Bhattacharya , Shirshendu Ganguly , Xuancheng Shao , Yufei Zhao

Heavy-tailed fluctuations and power law statistics pervade physics, finance, and economics, yet their origin is often ascribed to systems poised near criticality. Here we show that such behavior can emerge far from instability through a…

Statistical Mechanics · Physics 2025-10-15 Virgile Troude , Didier Sornette

The fermion determinant in an instanton background for a quark field of arbitrary mass is studied using the Schwinger proper-time representation with WKB scattering phase shifts for the relevant partial-wave differential operators.…

High Energy Physics - Theory · Physics 2007-05-23 Gerald V. Dunne , Jin Hur , Choonkyu Lee , Hyunsoo Min

We consider the eigenvalues and eigenvectors of finite, low rank perturbations of random matrices. Specifically, we prove almost sure convergence of the extreme eigenvalues and appropriate projections of the corresponding eigenvectors of…

Probability · Mathematics 2012-03-19 Florent Benaych-Georges , Raj Rao Nadakuditi
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