基于大维随机矩阵极限谱分布的面板数据单位根检验

近30年来,随着经济现象的越来越复杂和计算机信息技术的发展,大维面板数据模型被引入计量经济研究中.由于面板数据同一维的时间序列一样具有跨时间的特点,因而需要在进行回归之前考虑其是否平稳,即要对其进行单位根检验,以避免伪回归.基于大维随机矩阵的极限谱分布理论,给出针对面板数据的、一种新的单位根检验方法.     

On the Spectral Distribution of Gaussian Random Matrices

We consider the empirical spectral distribution （ESD） of a random matrix from the Gaussian Unitary Ensemble. Based on the Plancherel-Rotaeh approximation formula for Hermite polynomials, we prove that the expected empirical spectral distribution converges at the rate of O（n^-1） to the Wigner distribution function uniformly on every compact intervals [u,v] within the limiting support （-1, 1）. Furthermore, the variance of the ESD for such an interval is proved to be （πn）^-2 logn asymptotically which surprisingly enough, does not depend on the details （e.g. length or location） of the interval, This property allows us to determine completely the covariance function between the values of the ESD on two intervals.     

关于一类随机矩阵特征根极限分布的注记

一 引言 考虑k个p维总体X_1,X_2,…,X_k。假定它们都服从正态分布,其均值向量分别是(ξ_(1i),ξ_(2i),…,ξ_(pi)),i=1,2,…,k,且具有共同的协方差矩阵∑=(σ_(ij)),i,j=1,2,…,p。考虑矩阵     

Limiting Behavior of Random Continued Fractions

Let K(a _n /b _n ) be a continued fraction with elements (a _n ,b _n ) picked randomly and independently from $(\mathbb{C}\setminus\{0\})\times\mathbb{C}$ according to some probability distribution μ. We find sufficient conditions on μ for K(a _n /b _n ) to converge with probability 1 or to be restrained with probability 1. More generally, we also consider μ-random sequences {τ _n } of independent Möbius transformations and find sufficient conditions for $\{\tau_{1}\circ\tau_{2}\circ\cdots\circ\tau_{n}\}_{n=1}^{\infty}$ to converge or be restrained with probability 1. The analysis is based on an important paper by Furstenberg.     

具有随机足标的弱相依高斯序列最大值的极限分布

设ξ1,ξ2,…为标准化的平稳高斯序列.Mn=max.ξi,协方差Υn=Cov(ξ1,ξn+1)=E(ξ1ξn+1).{N(n)}为-列取正整数的随机变量,满足 0,n→∞.在条件Υnlogn→0,n→∞之下,给出了 MN(n)的极限分布.     

Generalized Random Spectral Measures

In an attempt to examine the random version of the spectral theorem, the notion of random spectral measures and generalized random spectral measures are introduced and investigated. It is shown that each generalized random spectral measure on $(\mathbb C ,\mathcal{B}(\mathbb C ))$ admits a modification which is a random spectral measure.     

随机图的谱矩

简单图G的k阶谱矩定义为G的特征值的k阶幂之和,记为Mk(G).应用概率和代数的方法,对于几乎所有的图G,本文给出Mk(G)的一个精确估计.此外,对于几乎所有的多部图G,本文给出了Mk(G)的上界和下界.     

随机环境中可逗留随机游动的强极限边界

假定{(αi,βi),αi,βi∈(0,1),i∈Z}是一列i.i.d.的随机变量,γi=1-αi-βi,称{(αi,γi,βi),i∈Z}为随机环境.在这个环境上定义一个随机游动{Xk}(称为随机环境中可逗留随机游动):当在x状态时,它以概率αx向右游走一步,以概率βx向左游走一步,或者以概率γx逗留.本文获得了该过程能够游走的最大值的强极限边界.     

NOD随机变量加权和的极限

The strong laws of large numbers and laws of the single logarithm for weighted sums of NOD random variables are established.The results presented generalize the corresponding results of Chen and Gan [5] in independent sequence case.     

Spectral Measures of Spiked Random Matrices

Journal of Theoretical Probability - We study two spiked models of random matrices under general frameworks corresponding, respectively, to additive deformation of random symmetric matrices and...     

Random Regularization of Brown Spectral Measure

We generalize a recent result of Haagerup; namely, we show that a convolution with a standard Gaussian random matrix regularizes the behavior of Fuglede-Kadison determinant and Brown spectral distribution measure. In this way, it is possible to establish a connection between the limit eigenvalues distributions of a wide class of random matrices and the Brown measure of the corresponding limits.     

NA随机变量加权和的极限结果

该文研究了NA随机变量序列加权和的Marcinkiewcz Zygmund 强大数定律和完全收敛性.这些结果推广和完善了Bai和Cheng[1]和Cuzick[2]的结果.     

Canonical Moments and Random Spectral Measures

We study some connections between the random moment problem and random matrix theory. A uniform draw in a space of moments can be lifted into the spectral probability measure of the pair (A,e), where A is a random matrix from a classical ensemble, and e is a fixed unit vector. This random measure is a weighted sampling among the eigenvalues of A. We also study the large deviations properties of this random measure when the dimension of the matrix increases. The rate function for these large deviations involves the reversed Kullback information.     

Limiting Spectral Radii of Circular Unitary Matrices Under Light Truncation

Journal of Theoretical Probability - Consider a truncated circular unitary matrix which is a $$p_n$$ by $$p_n$$ submatrix of an n by n circular unitary matrix after deleting the last $$n-p_n$$...     

Limiting Behavior of High Order Correlations for Simple Random Sampling

For ${N = 1, 2,\ldots,}$ let S _N be a simple random sample of size n = n _N from a population A _N of size N, where ${0 \leq n \leq N}$ . Then with f _N =  n/N, the sampling fraction, and 1 _A the inclusion indicator that ${A \in S_N}$ , for any ${H \subset A_N}$ of size ${k \geq 0}$ , the high order correlations $${\rm Corr}(k) = E \big(\mathop{\Pi}\limits_{A \in H} ({\bf 1}_A - f_N )\big)$$ depend only on k, and if the sampling fraction ${f_N \rightarrow f}$ as ${N \rightarrow \infty}$ , then $$N^{k/2}{\rm Corr}(k) \rightarrow [f (f - 1)]^{k/2}EZ^{k}, k \,{\rm even}$$ and $$N^{(k+1)/2}{\rm Corr}(k) \rightarrow [f (f - 1)]^{(k-1)/2}(2f - 1)\frac{1}{3}(k - 1)EZ^{k+1}, k \,{\rm odd}$$ where Z is a standard normal random variable. This proves a conjecture given in [2].     

Spectral Radii of Large Non-Hermitian Random Matrices

By using the independence structure of points following a determinantal point process, we study the radii of the spherical ensemble, the truncation of the circular unitary ensemble and the product ensemble with parameters n and k. The limiting distributions of the three radii are obtained. They are not the Tracy–Widom distribution. In particular, for the product ensemble, we show that the limiting distribution has a transition phenomenon: When \(k/n\rightarrow 0\), \(k/n\rightarrow \alpha \in (0,\infty )\) and \(k/n\rightarrow \infty \), the liming distribution is the Gumbel distribution, a new distribution \(\mu \) and the logarithmic normal distribution, respectively. The cumulative distribution function (cdf) of \(\mu \) is the infinite product of some normal distribution functions. Another new distribution \(\nu \) is also obtained for the spherical ensemble such that the cdf of \(\nu \) is the infinite product of the cdfs of some Poisson-distributed random variables.     

The Limiting Spectral Measure for Ensembles of Symmetric Block Circulant Matrices

Given an ensemble of N×N random matrices, a natural question to ask is whether or not the empirical spectral measures of typical matrices converge to a limiting spectral measure as N→∞. While this has been proved for many thin patterned ensembles sitting inside all real symmetric matrices, frequently there is no nice closed form expression for the limiting measure. Further, current theorems provide few pictures of transitions between ensembles. We consider the ensemble of symmetric m-block circulant matrices with entries i.i.d.r.v. These matrices have toroidal diagonals periodic of period m. We view m as a “dial” we can “turn” from the thin ensemble of symmetric circulant matrices, whose limiting eigenvalue density is a Gaussian, to all real symmetric matrices, whose limiting eigenvalue density is a semi-circle. The limiting eigenvalue densities f _m show a visually stunning convergence to the semi-circle as m→∞, which we prove. In contrast to most studies of patterned matrix ensembles, our paper gives explicit closed form expressions for the densities. We prove that f _m is the product of a Gaussian and a certain even polynomial of degree 2m?2; the formula is the same as that for the m×m Gaussian Unitary Ensemble (GUE). The proof is by derivation of the moments from the eigenvalue trace formula. The new feature, which allows us to obtain closed form expressions, is converting the central combinatorial problem in the moment calculation into an equivalent counting problem in algebraic topology. We end with a generalization of the m-block circulant pattern, dropping the assumption that the m random variables be distinct. We prove that the limiting spectral distribution exists and is determined by the pattern of the independent elements within an m-period, depending not only on the frequency at which each element appears, but also on the way the elements are arranged.     

算子稳定随机向量序列的一些极限结果

对于独立同分布的没有Gauss分量的指数为可逆线性算子A的算子稳定的R~d值随机向量序列,本文通过积分检验讨论了其部分和及加权和(包括一些经典的加权和,如Cesàro加权和,后置和方式,Euler可和方式,Borel可和方式,几何加权和等)的极限结果.由此得到了部分和及加权和在相对于A的谱分解下的Chover型重对数律,这是与A的特征值的实部有关的结果.