Fanny Augeri, Raphael Butez, Ofer Zeitouni
We prove a central limit theorem for the logarithm of the characteristic polynomial of random Jacobi matrices. Our results cover the G$β$E models for $β>0$.
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We prove a central limit theorem (CLT) for the product of a class of random singular matrices related to a random Hill's equation studied by Adams$\unicode{x2013}$Bloch$\unicode{x2013}$Lagarias. The CLT features an explicit formula for the variance in terms of the distribution of the matrix entries and this allows for exact calculation in some examples. Our proof relies on a novel connection to the theory of $m$-dependent sequences which also leads to an interesting and precise nondegeneracy condition.
We consider linear spectral statistics of the form $\mathrm{tr} ( φ(H))$ for test functions $φ$ of low regularity and Wigner matrices $H$ with smooth entry distribution. We show that for functions $φ$ in the Sobolev space $H^{1/2+\varepsilon}$ or the space $C^{1/2+\varepsilon}$, that are supported within the spectral bulk of the semicircle distribution, these linear spectral statistics have asymptotic Gaussian fluctuations with the same variance as in the CLT for functions of higher regularity, for any $\varepsilon >0$.
In this paper we show a functional central limit theorem for the sum of the first $\lfloor t n \rfloor$ diagonal elements of $f(Z)$ as a function in $t$, for $Z$ a random real symmetric or complex Hermitian $n\times n$ matrix. The result holds for orthogonal or unitarily invariant distributions of $Z$, in the cases when the linear eigenvalue statistic $\operatorname{tr} f(Z)$ satisfies a CLT. The limit process interpolates between the fluctuations of individual matrix elements as $f(Z)_{1,1}$ and of the linear eigenvalue statistic. It can also be seen as a functional CLT for processes of rando…
We show that the variance of centred linear statistics of eigenvalues of GUE matrices remains bounded for large $n$ for some classes of test functions less regular than Lipschitz functions. This observation is suggested by the limiting form of the variance (which has previously been computed explicitly), but it does not seem to appear in the literature. We combine this fact with comparison techniques following Tao-Vu and Erdös, Yau, et al. and a Littlewood-Paley type decomposition to extend the central limit theorem for linear eigenvalue statistics to functions in the Hölder class $C^{1/2+ε}$ …
This paper investigates the central limit theorem for linear spectral statistics of high dimensional sample covariance matrices of the form $\mathbf{B}_n=n^{-1}\sum_{j=1}^{n}\mathbf{Q}\mathbf{x}_j\mathbf{x}_j^{*}\mathbf{Q}^{*}$ where $\mathbf{Q}$ is a nonrandom matrix of dimension $p\times k$, and $\{\mathbf{x}_j\}$ is a sequence of independent $k$-dimensional random vector with independent entries, under the assumption that $p/n\to y>0$. A key novelty here is that the dimension $k\ge p$ can be arbitrary, possibly infinity. This new model of sample covariance matrices $\mathbf{B}_n$ covers mos…
For large dimensional non-Hermitian random matrices $X$ with real or complex independent, identically distributed, centered entries, we consider the fluctuations of $f(X)$ as a matrix where $f$ is an analytic function around the spectrum of $X$. We prove that for a generic bounded square matrix $A$, the quantity $\mathrm{Tr}f(X)A$ exhibits Gaussian fluctuations as the matrix size grows to infinity, which consists of two independent modes corresponding to the tracial and traceless parts of $A$. We find a new formula for the variance of the traceless part that involves the Frobenius norm of $A$ …
We consider the spectral properties of a class of regularized estimators of (large) empirical covariance matrices corresponding to stationary (but not necessarily Gaussian) sequences, obtained by banding. We prove a law of large numbers (similar to that proved in the Gaussian case by Bickel and Levina), which implies that the spectrum of a banded empirical covariance matrix is an efficient estimator. Our main result is a central limit theorem in the same regime, which to our knowledge is new, even in the Gaussian setup.
A central limit theorem (CLT) for the smoothed empirical spectral distribution of sample covariance matrices is established. Moreover, the CLTs for the smoothed quantiles of Marcenko and Pastur's law have been also developed.
CLT for generalized patterned random matrices: a unified approach
本文提出统一方法证明广义模式随机矩阵线性特征值统计量在偶次检验函数下收敛于高斯分布，推广了已有中心极限定理，并给出奇次情形下可能非高斯收敛的极限矩结果。
We prove a CLT for spectra of submatrices of real symmetric and Hermitian Wigner matrices. We show that if in the standard normalization the fourth moment of the off-digonal entries is GOE/GUE-like then the limiting Gaussian process can be viewed as a collection of simply yet nontrivially correlated two-dimensional Gaussian Free Fields.
Consider a $N\times n$ random matrix $Y_n=(Y_{ij}^{n})$ where the entries are given by $$ Y_{ij}^{n}=\frac{σ_{ij}(n)}{\sqrt{n}} X_{ij}^{n} $$ the $X_{ij}^{n}$ being centered, independent and identically distributed random variables with unit variance and $(σ_{ij}(n); 1\le i\le N, 1\le j\le n)$ being an array of numbers we shall refer to as a variance profile. We study in this article the fluctuations of the random variable $$ \log\det(Y_n Y_n^* + ρI_N) $$ where $Y^*$ is the Hermitian adjoint of $Y$ and $ρ> 0$ is an additional parameter. We prove that when centered and properly rescaled, this r…
For $N,n\in\mathbb N$, consider the sample covariance matrix $$S_N(T)=\frac{1}{N}XX^*$$ from a data set $X=C_N^{1/2}ZT_n^{1/2}$, where $Z=(Z_{i,j})$ is a $N\times n$ matrix having i.i.d. entries with mean zero and variance one, and $C_N, T_n$ are deterministic positive semi-definite Hermitian matrices of dimension $N$ and $n$, respectively. We assume that $(C_N)_N$ is bounded in spectral norm, and $T_n$ is a Toeplitz matrix with its largest eigenvalues diverging to infinity. The matrix $X$ can be viewed as a data set of an $N$-dimensional long memory stationary process having separable depende…
We present some applications of central limit theorems on mesoscopic scales for random matrices. When combined with the recent theory of "homogenization" for Dyson Brownian Motion, this yields the universality of quantities which depend on the behavior of single eigenvalues of Wigner matrices and $β$-ensembles. Among the results we obtain are the Gaussian fluctuations of single eigenvalues for Wigner matrices (without an assumption of 4 matching moments) and classical $β$-ensembles ($β=1, 2, 4$), Gaussian fluctuations of the eigenvalue counting function, and an asymptotic expansion up to order…
An edge CLT for the log determinant of Laguerre beta ensembles
本文针对尺度化 Laguerre β 系综，在谱上边缘附近建立了对数行列式的中心极限定理，并将结果推广到 LUE 和 LOE 情形，为临界尖峰协方差矩阵检验和球面自旋玻璃自由能分析提供了理论工具。
We consider three matrix models of order 2 with one random entry $ε$ and the other three entries being deterministic. In the first model, we let $ε\sim\textrm{Bernoulli}\left(\frac{1}{2}\right)$. For this model we develop a new technique to obtain estimates for the top Lyapunov exponent in terms of a multi-level recursion involving Fibonacci-like sequences. This in turn gives a new characterization for the Lyapunov exponent in terms of these sequences. In the second model, we give similar estimates when $ε\sim\textrm{Bernoulli}\left(p\right)$ and $p\in [0,1]$ is a parameter. Both of these mode…
We study the renormalized real sample covariance matrix $H=X^TX/\sqrt{MN}-\sqrt{M/N}$ with $N/M\rightarrow0$ as $N, M\rightarrow \infty$ in this paper. And we always assume $M=M(N)$. Here $X=[X_{jk}]_{M\times N}$ is an $M\times N$ real random matrix with i.i.d entries, and we assume $\mathbb{E}|X_{11}|^{5+δ}<\infty$ with some small positive $δ$. The Stieltjes transform $m_N(z)=N^{-1}Tr(H-z)^{-1}$ and the linear eigenvalue statistics of $H$ are considered. We mainly focus on the asymptotic expansion of $\mathbb{E}\{m_N(z)\}$ in this paper. Then for some fine test function, a central limit theor…
CLT for LES of real valued random centrosymmetric matrices
本文研究实值大型中心对称随机矩阵线性特征值统计量的涨落，建立相依变量和的CLT，并通过加权图重叠环计数的组合论证给出极限高斯分布的方差表达式。
We show that the global fluctuations of spectra of GOE and GUE matrices and their principal submatrices executing Dyson's Brownian motion are Gaussian in the limit of large matrix dimensions. For nested submatrices one obtains a limiting three-dimensional generalized Gaussian process; its restrictions to two-dimensional sections that are monotone in matrix sizes and time moments coincide with the two-dimensional Gaussian Free Field with zero boundary conditions. The proof is by moment convergence, and it extends to more general Wigner matrices and their stochastic evolution.
For $k,m,n\in \mathbb{N}$, we consider $n^k\times n^k$ random matrices of the form $$ \mathcal{M}_{n,m,k}(\mathbf{y})=\sum_{α=1}^mτ_α{Y_α}Y_α^T,\quad Y_α=\mathbf{y}_α^{(1)}\otimes...\otimes\mathbf{y}_α^{(k)}, $$ where $τ_{α}$, $α\in[m]$, are real numbers and $\mathbf{y}_α^{(j)}$, $α\in[m]$, $j\in[k]$, are i.i.d. copies of a normalized isotropic random vector $\mathbf{y}\in \mathbb{R}^n$. For every fixed $k\ge 1$, if the Normalized Counting Measures of $\{τ_{α}\}_α$ converge weakly as $m,n\rightarrow \infty$, $m/n^k\rightarrow c\in \lbrack 0,\infty )$ and $\mathbf{y}$ is a good vector in the se…
Spectrum of random centrosymmetric matrices; CLT and Circular law
本文研究随机中心对称矩阵的线性特征值统计量，证明其渐近服从正态分布，并给出方差精确表达式，同时论证极限谱分布遵循圆律。
In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix when the population covariance matrices are not uniformly bounded, which is a nontrivial extension of the Bai-Silverstein theorem (BST) (2004). The latter has strongly stimulated the development of high-dimensional statistics, especially the application of random matrix theory to statistics. However, the assumption of uniform boundedness of the population covariance matrices is found strongly limited to the applications of BST. The aim of this paper is …
As an important topic in Mathematical Physics and statistics, random matrices theory has found uses in many aspects of modern physics and multivariate analysis. This paper is to investigate the Gaussian fluctuations for linear spectral statistics (LSS) of Wigner beta ensembles. We first establish a central limit theorem (CLT) for LSS of Wigner quaternion matrices, then give a general CLT for Wigner $β$ ensembles.
We show central limit theorems (CLT) for the Stieltjes transforms or more general analytic functions of symmetric matrices with independent heavy tailed entries, including entries in the domain of attraction of $α$-stable laws and entries with moments exploding with the dimension, as in the adjacency matrices of Erdös-Rényi graphs. For the second model, we also prove a central limit theorem of the moments of its empirical eigenvalues distribution. The limit laws are Gaussian, but unlike to the case of standard Wigner matrices, the normalization is the one of the classical CLT for independent r…
Under the high-dimensional setting that data dimension and sample size tend to infinity proportionally, we derive the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix. Different from existing literature, our results do not require the assumption that the population covariance matrices are bounded. Moreover, many common kernel functions in the real data such as logarithmic functions and polynomial functions are allowed in this paper. In our model, the number of spiked eigenvalues can be fixed or tend to infinity. One salient feature …
We provide bounds of Berry-Esseen type for fundamental limit theorems in operator-valued free probability theory such as the operator-valued free Central Limit Theorem and the asymptotic behaviour of distributions of operator-valued matrices. Our estimates are on the level of operator-valued Cauchy transforms and the Lévy distance. We address the single-variable as well as the multivariate setting for which we consider linear matrix pencils and noncommutative polynomials as test functions. The estimates are in terms of operator-valued moments and yield the first quantitative bounds on the Lévy…
A law of large numbers and a central limit theorem are derived for linear statistics of random symmetric matrices whose on-or-above diagonal entries are independent, but neither necessarily identically distributed, nor necessarily all of the same variance. The derivation is based on systematic combinatorial enumeration, study of generating functions, and concentration inequalities of the Poincare type. Special cases treated, with an explicit evaluation of limiting variances, are generalized Wigner and Wishart matrices.
Ultra high order cumulants and quantitative CLT for polynomials in Random Matrices
本文研究随机矩阵多项式的超高阶累积量上界，并由此推导出任意自伴多项式迹的三类定量中心极限定理，包括Cramér型修正、Berry-Esseen界和浓度不等式。
Mesoscopic Spectral CLT for Block Correlated Random Matrices
本文研究了块相关随机矩阵的线性特征值统计量涨落，利用矩阵值 Dyson 方程的两点版本证明其在所有介观尺度上呈现高斯性，且方差具有与高斯正交或酉系综一致的普适性。
Spiked eigenvalues of high-dimensional sample autocovariance matrices: CLT and applications
本文建立高维样本自协方差矩阵尖峰特征值的 CLT，并据此提出尖峰特征值等价性检验及层次聚类方法，应用于多国死亡率数据。
In this article, we establish a near-optimal convergence rate for the CLT of linear eigenvalue statistics of Wigner matrices, in Kolmogorov-Smirnov distance. For all test functions $f\in C^5(\mathbb R)$, we show that the convergence rate is either $N^{-1/2+\varepsilon}$ or $N^{-1+\varepsilon}$, depending on the first Chebyshev coefficient of $f$ and the third moment of the diagonal matrix entries. The condition that distinguishes these two rates is necessary and sufficient. For a general class of test functions, we further identify matching lower bounds for the convergence rates. In addition, …
Two CLTs for Sparse Random Matrices
本文通过矩方法证明了稀疏Erdös-Rényi图邻接矩阵特征值统计量的中心极限定理，揭示了最大特征值在更广的稀疏范围内具有高斯波动。
A CLT for the difference of eigenvalue statistics of sample covariance matrices
该研究在数据维度与样本量同阶增长的情形下，证明了样本协方差矩阵与删去一列及对应行后矩阵的线性谱统计量之差的中心极限定理，放宽了总体协方差矩阵对角性与各阶矩存在的条件，还建立了差分型谱统计量与常规线性谱统计量的渐近独立性。
Functional CLT for general sample covariance matrices
本文研究了在测试函数属于C³类时，广义样本协方差矩阵的线性谱统计量的中心极限定理，通过伯恩斯坦多项式近似证明在一定条件下中心化统计量具有高斯极限，并在更强条件下建立收敛速度。
In this article, we study the fluctuations of the random variable: $$ {\mathcal I}_n(ρ) = \frac 1N \log\det(Σ_n Σ_n^* + ρI_N),\quad (ρ>0) $$ where $Σ_n= n^{-1/2} D_n^{1/2} X_n\tilde D_n^{1/2} +A_n$, as the dimensions of the matrices go to infinity at the same pace. Matrices $X_n$ and $A_n$ are respectively random and deterministic $N\times n$ matrices; matrices $D_n$ and $\tilde D_n$ are deterministic and diagonal, with respective dimensions $N\times N$ and $n\times n$; matrix $X_n=(X_{ij})$ has centered, independent and identically distributed entries with unit variance, either real or comple…
We study the central limit theorem for sums of independent tensor powers, $\frac{1}{\sqrt{d}}\sum\limits_{i=1}^d X_i^{\otimes p}$. We focus on the high-dimensional regime where $X_i \in \mathbb{R}^n$ and $n$ may scale with $d$. Our main result is a proposed threshold for convergence. Specifically, we show that, under some regularity assumption, if $n^{2p-1}\gg d$, then the normalized sum converges to a Gaussian. The results apply, among others, to symmetric uniform log-concave measures and to product measures. This generalizes several results found in the literature. Our main technique is a no…
Let $ν\in M^1([0,\infty[)$ be a fixed probability measure. For each dimension $p\in\b N$, let $(X_n^p)_{n\ge1}$ be i.i.d. $\b R^p$-valued radial random variables with radial distribution $ν$. We derive two central limit theorems for $ \|X_1^p+...+X_n^p\|_2$ for $n,p\to\infty$ with normal limits. The first CLT for $n>>p$ follows from known estimates of convergence in the CLT on $\b R^p$, while the second CLT for $n<<p$ will be a consequence of asymptotic properties of Bessel convolutions. Both limit theorems are considered also for $U(p)$-invariant random walks on the space of $p\times q$ matri…
We consider high dimensional Wishart matrices $\mathbb{X} \mathbb{X}^{\top}$ where the entries of $\mathbb{X} \in {\mathbb{R}^{n \times d}}$ are i.i.d. from a log-concave distribution. We prove an information theoretic phase transition: such matrices are close in total variation distance to the corresponding Gaussian ensemble if and only if $d$ is much larger than $n^3$. Our proof is entropy-based, making use of the chain rule for relative entropy along with the recursive structure in the definition of the Wishart ensemble. The proof crucially relies on the well known relation between Fisher i…
Spectrum of Random Matrices with Exploding Moments
研究具有爆炸矩矩阵模型的线性特征值统计的中心极限定理，采用渐近 Wick 公式证明椭圆、中心对称、循环和互相关块矩阵的 CLT。
We study the fluctuations of certain biorthogonal ensembles for which the underlying family \{P,Q\} satisfies a finite-term recurrence relation of the form $x P(x) = \mathbf{J}P(x)$. For polynomial linear statistics of such ensembles, we reformulate the cumulants' method introduced by Soshnikov in terms of counting lattice paths on the graph of the adjacency matrix \mathbf{J}. In the spirit of Breuer-Duits, we show that the asymptotic fluctuations of polynomial linear statistics are described by the right-limits of the matrix \mathbf{J}. Moreover, whenever the right-limit is a Laurent matrix, …
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 distribution, Marchenko-Pastur distributions or Kesten-Mckey distributions, respectively. The Gaussian fluctuation around the limit is then investigated.
Maximum of the characteristic polynomial of random Jacobi matrices
本文研究了满足指数可积条件的随机雅可比矩阵特征多项式最大值的二级渐进行为，并通过三对角表示方法验证了Fydorov-Simm猜想。
CLT for LES of correlated Non-Hermitian Random Matrices
本文研究了两个相关非厄米随机矩阵的联合谱渐近行为，证明在特定条件下其线性特征值统计量联合收敛于二元高斯分布，并推广了已有结果。
Mesoscopic Edge Universality of Orthogonal Polynomial Ensembles
本文研究正交多项式系综边缘的介观涨落，证明在递推系数缓慢变化时线性统计量满足普适的中心极限定理，并给出截断雅可比矩阵预解式的估计，适用于雅可比、拉盖尔、高斯酉及随机平铺离散系综。
Necessary and sufficient condition for CLT of linear spectral statistics of sample correlation matrices
本文针对具有无穷四阶矩的样本相关矩阵，给出了线性谱统计量中心极限定理的充要条件，并证明尾指数决定渐近行为。
CLT for non-Hermitian random band matrices with variance profiles
本文研究了具有连续方差剖面的非厄米随机带矩阵的线性特征值统计量波动，证明了其收敛于高斯分布，并给出了不同带宽增长条件下的方差显式公式及其极限关系。
We present a unified approach to a couple of central limit theorems for radial random walks on hyperbolic spaces and time-homogeneous Markov chains on the positive half line whose transition probabilities are defined in terms of the Jacobi convolutions. The proofs of all results are based on limit results for the associated Jacobi functions. In particular, we consider the cases where the first parameter (i.e., the dimension of the hyperbolic space) tends to infinity as well as the cases $ϕ_{iρ-λ}^{(α,β)}(t)$ for small $λ$, and $ϕ_{iρ-nλ}^{(α,β)}(t/n)$ for $n\to\infty$. The proofs of all these …
On the rate of convergence in the CLT for LSS of large-dimensional sample covariance matrices
本文利用 Stein 方法研究大维样本协方差矩阵线性谱统计量中心极限定理的收敛速度，在十阶矩有限且维数与样本量之比趋于正常数的条件下，证明其 Kolmogorov-Smirnov 距离收敛速度为 $O(n^{-1/2+κ})$。
CLT for Linear Spectral Statistics in High-Dimensional Random Effects Models
本文研究多层级方差分量样本协方差矩阵的线性谱统计量，在高维比例增长情形下证明其中心极限定理，并给出可数值计算的均值和协方差，为数量遗传学中嵌套随机效应模型的高维协方差估计提供理论基础。
Non-Gaussian fluctuations for traces of squared sample correlation matrices in high dimensions
该研究针对高维样本相关矩阵平方的迹，在不同正则变化指数α下分析其波动的高斯性，推导了普适与非普适中心极限定理，并通过模拟验证结果。
CLT for $β$ ensembles at high-temperature, and for integrable systems: a transfer operator approach
该研究采用转移算子方法，证明了若干可积模型与高温多项式势β系综的多项式中心极限定理，关联了两类系统Lax矩阵矩的统计量，证明可积系统局部函数空间相关指数衰减，并建立了Berry–Esseen型界。
Bessel processes $(X_{t,k})_{t\ge0}$ in $N$ dimensions are classified via associated root systems and multiplicity constants $k\ge0$. They describe interacting Calogero-Moser-Suther\-land particle systems with $N$ particles and are related to $β$-Hermite and $β$-Laguerre ensembles. Recently, several central limit theorems were derived for fixed $t>0$, fixed starting points, and $k\to\infty$. In this paper we extend the CLT in the A-case from start in 0 to arbitrary starting distributions by using a limit result for the corresponding Bessel functions. We also determine the eigenvalues and eigen…
An explicit formula for the mean spectral measure of a random Jacobi matrix is derived. The matrix may be regarded as the limit of Gaussian beta ensemble (G$β$E) matrices as the matrix size $N$ tends to infinity with the constraint that $N β$ is a constant.
We present an unified framework to identify spectra of Jacobi matrices. We give applications to long-standing conjecture of Chihara concerning one-quarter class of orthogonal polynomials, to the conjecture posed by Roehner and Valent concerning continuous spectra of generators of birth and death processes and to spectral properties of operators studied by Janas, Moszyński and Pedersen.
Consider a random vector $\mathbf{y}=\mathbfΣ^{1/2}\mathbf{x}$, where the $p$ elements of the vector $\mathbf{x}$ are i.i.d. real-valued random variables with zero mean and finite fourth moment, and $\mathbfΣ^{1/2}$ is a deterministic $p\times p$ matrix such that the spectral norm of the population correlation matrix $\mathbf{R}$ of $\mathbf{y}$ is uniformly bounded. In this paper, we find that the log determinant of the sample correlation matrix $\hat{\mathbf{R}}$ based on a sample of size $n$ from the distribution of $\mathbf{y}$ satisfies a CLT (central limit theorem) for $p/n\to γ\in (0, 1…
Motivated by recent results in random matrix theory we will study the distributions arising from products of complex Gaussian random matrices and truncations of Haar distributed unitary matrices. We introduce an appropriately general class of measures and characterize them by their moments essentially given by specific Jacobi polynomials with varying parameters. Solving this moment problem requires a study of the Riemann surfaces associated to a class of algebraic equations. The connection to random matrix theory is then established using methods from free probability.
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