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We investigate random density matrices obtained by partial tracing larger random pure states. We show that there is a strong connection between these random density matrices and the Wishart ensemble of random matrix theory. We provide…

Quantum Physics · Physics 2009-05-14 Ion Nechita

The product of M complex random Gaussian matrices of size N has recently been studied by Akemann, Kieburg and Wei. They showed that, for fixed M and N, the joint probability distribution for the squared singular values of the product matrix…

Mathematical Physics · Physics 2015-06-15 Lun Zhang

The noncentral Wishart distribution has become more mainstream in statistics as the prevalence of applications involving sample covariances with underlying multivariate Gaussian populations as dramatically increased since the advent of…

Statistics Theory · Mathematics 2022-05-25 Frédéric Ouimet

The eigenvalue densities of two random matrix ensembles, the Wigner Gaussian matrices and the Wishart covariant matrices, are decomposed in the contributions of each individual eigenvalue distribution. It is shown that the fluctuations of…

Mathematical Physics · Physics 2010-08-16 O. Bohigas , M. P. Pato

In this paper, we investigate the limiting spectral distribution of the sample correlation matrix, whose sample vectors are $k$-fold tensor products of $n$-dimensional vectors with i.i.d. entries. We focus on the limiting regime $n,k \to…

Probability · Mathematics 2026-05-28 Wangjun Yuan

We investigate the asymptotic behavior of the empirical eigenvalues distribution of the partial transpose of a random quantum state. The limiting distribution was previously investigated via Wishart random matrices indirectly (by…

Mathematical Physics · Physics 2013-07-16 Motohisa Fukuda , Piotr Śniady

Random matrix theory has become a cornerstone in modern statistics and data science, providing fundamental tools for understanding high-dimensional covariance structures. Within this framework, the Wishart matrix plays a central role in…

Statistics Theory · Mathematics 2025-11-26 Fengcheng Liu

The paper studies the limiting behavior of spectral measures of random Jacobi matrices of Gaussian, Wishart and MANOVA beta ensembles. We show that the spectral measures converge weakly to a limit distribution which is the semicircle…

Probability · Mathematics 2017-10-12 Trinh Khanh Duy

Recently, D. Wang has devised a new contour integral based method to simplify certain matrix integrals. Capitalizing on that approach, we derive a new expression for the probability density function (p.d.f.) of the joint eigenvalues of a…

Statistics Theory · Mathematics 2013-06-28 Prathapasinghe Dharmawansa

A Wishart matrix is said to be spiked when the underlying covariance matrix has a single eigenvalue $b$ different from unity. As $b$ increases through $b=2$, a gap forms from the largest eigenvalue to the rest of the spectrum, and with…

Mathematical Physics · Physics 2014-07-01 Peter J. Forrester

We briefly review the solution of three ensembles of non-Hermitian random matrices generalizing the Wishart-Laguerre (also called chiral) ensembles. These generalizations are realized as Gaussian two-matrix models, where the complex…

Mathematical Physics · Physics 2011-06-01 Gernot Akemann

Given a sample $X_0,...,X_{n-1}$ from a $d$-dimensional stationary time series $(X_t)_{t \in \mathbb{Z}}$, the most commonly used estimator for the spectral density matrix $F(\theta)$ at a given frequency $\theta \in [0,2\pi)$ is the…

Statistics Theory · Mathematics 2025-06-09 Ben Deitmar

In random matrix theory, Marchenko-Pastur law states that random matrices with independent and identically distributed entries have a universal asymptotic eigenvalue distribution under large dimension limit, regardless of the choice of…

High Energy Physics - Theory · Physics 2015-05-12 Xiaochuan Lu , Hitoshi Murayama

We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random…

Data Analysis, Statistics and Probability · Physics 2008-12-02 Jean-Philippe Bouchaud , Laurent Laloux , M. Augusta Miceli , Marc Potters

We investigate the real eigenvalues of asymmetric Wishart matrices of size $N$, indexed by the rectangular parameter $\nu \in \mathbb{N}$ and the non-Hermiticity parameter $\tau \in [0,1]$. The rectangular parameter $\nu$ is either fixed or…

Probability · Mathematics 2025-03-20 Sung-Soo Byun , Kohei Noda

We show that a degenerate neutrino mass spectrum can be realized in the neutrino mass anarchy hypothesis, if the neutrino Yukawa and right-handed neutrino mass matrices are given by the Wishart matrix, i.e. products of $N \times 3$…

High Energy Physics - Phenomenology · Physics 2015-06-24 Kwang Sik Jeong , Naoya Kitajima , Fuminobu Takahashi

We equip the polytope of $n\times n$ Markov matrices with the normalized trace of the Lebesgue measure of $\mathbb{R}^{n^2}$. This probability space provides random Markov matrices, with i.i.d. rows following the Dirichlet distribution of…

Probability · Mathematics 2010-06-16 Djalil Chafai

Using Beck and Cohen's superstatistics, we introduce in a systematic way a family of generalised Wishart-Laguerre ensembles of random matrices with Dyson index $\beta$ = 1,2, and 4. The entries of the data matrix are Gaussian random…

Mathematical Physics · Physics 2009-04-07 A. Y. Abul-Magd , G. Akemann , P. Vivo

We consider the singular values of certain Young diagram shaped random matrices. For block-shaped random matrices, the empirical distribution of the squares of the singular eigenvalues converges almost surely to a distribution whose moments…

Probability · Mathematics 2024-03-14 Fabio Deelan Cunden , Marilena Ligabò , Tommaso Monni

We study the nonparametric maximum likelihood estimator $\widehat{\pi}$ for Gaussian location mixtures in one dimension. It has been known since (Lindsay, 1983) that given an $n$-point dataset, this estimator always returns a mixture with…

Statistics Theory · Mathematics 2025-03-27 Yury Polyanskiy , Mark Sellke