椭球等高矩阵分布关于非奇异矩阵变换的不变性

本文首先将矩阵F分布和矩阵t分布的定义推广到左球分布类,其密度函数与产生它们的左球分布或球对称分布的密度均无关.然后讨论了椭球等高分布关于非奇异矩阵变换的不变性问题,包括矩阵Beta分布、逆矩阵Beta分布、矩阵Dirichlet分布、逆矩阵Dirichlet分布、矩阵F分布和矩阵t等分布.在非奇异变换下,这些分布的密度不但与产生它们的左球分布的密度函数无关,而且与非奇异变换矩阵无关.     

正态-逆Wishart先验信息下多元线性模型的后验似然比检验

在正态-逆Wishart先验信息下考虑多元正态线性模型Y-Nn×m(XB,In■∑)的参数矩阵B的线性假设检验问题,根据B的后验概率分布构造了关于B的两种线性假设的后验似然比检验,所得检验统计量是矩阵F-分布的特征值函数.     

线性模型参数向量的近似贝叶斯估计

用线性贝叶斯方法去同时估计线性模型中回归系数和误差方差,并在不知道先验分布具体形式的情况下,得到了线性贝叶斯估计的表达式.在均方误差矩阵准则下,证明了其优于最小二乘估计和极大似然估计.与利用MCMC算法得到的贝叶斯估计相比,线性贝叶斯估计具有显式表达式并且更方便使用.对于几种不同的先验分布,数值模拟结果表明线性贝叶斯估...     

正态-逆Wishart先验下多元线性模型中经验Bayes估计的优良性

在正态-逆Wishart先验下研究了多元线性模型中参数的经验Bayes估计及其优良性问题.当先验分布中含有未知参数时,构造了回归系数矩阵和误差方差矩阵的经验Bayes估计,并在Bayes均方误差(简称BMSE)准则和Bayes均方误差阵(简称BMSEM)准则下,证明了经验Bayes估计优于最小二乘估计.最后,进行了Monte Carlo模拟研究,进一步验证了理论结果.     

椭球等高矩阵分布的条件分布

本文给出了椭球等高矩阵分布的条件分布的随机表示,证明了椭球等高矩阵分布的条件分布仍是椭球等高分布。     

多重线性回归模型的贝叶斯预报分析

多重线性回归模型的贝叶斯预报分析是贝叶斯线性模型理论的重要组成部分。通过模型系统的统计结构，证明了矩阵正态-Wishart分布为模型参数的共轭先验分布；利用贝叶斯定理，根据模型的样本似然函数和参数的先验分布推导了参数的后验分布；然后，从数学上严格推断了模型的预报分布密度函数，证明了模型预报分布为矩阵t分布。研究结果表明：由于参数先验分布的作用，样本的预报分布与其原统计分布有着本质性的差异，前服从矩阵正态分布，而后为矩阵t分布。     

空间变系数模型的局部线性BGWR估计

针对空间变系数回归模型,通过空间加权距离构造权重矩阵,基于多元线性回归模型的贝叶斯统计推断,得到了该模型的局部线性BGWR估计方法.通过此方法推导出回归系数的后验分布,采用Gibbs抽样得到回归系数的逐点估计.将所得结果通过绘制曲面图、计算偏差均值和标准差均值与LeSage的BGWR模型估计结果进行对比,进一步说明估计方法的有效性.     

有缺失数据的正态母体参数的后验分布及其抽样算法

在缺失数据机制是可忽略的、先验分布是逆矩阵Γ分布的假设下,利用矩阵的cholesky分解和变量替换方法,本文导出了有单调缺失数据结构的正态分布参数的后验分布形式.进-步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法.     

从SPⅦ分布生成的新的多元偏态t分布的有关性质

随机向量的t分布属于椭球等高分布族,然而,它是对称分布.在许多诸如经济学、生理学、社会学等领域中,有时回归模型中的随机误差不再满足对称性,通常表现出高度的偏态性(skewness).于是就有了偏态椭球等高分布族.本文在已有的多元偏态t分布的基础上,着重研究它的分布性质,包括线性组合分布、边缘分布、条件分布及各阶矩.     

线性模型中回归系数和误差方差的同时Bayes估计的优良性

在线性模型中回归系数与误差方差具有正态-逆Gamma先验时,导出了回归系数与误差方差的同时Bayes估计.在均方误差矩阵准则和Bayes Pitman closeness准则下,研究了回归系数的Bayes估计相对于最小二乘(LS)估计的优良性,还讨论了误差方差的Bayes估计在均方误差准则下相对于LS估计的优良性.     

On singular matrix variate beta distribution

1.IntrodnctionThispaperextendsthestudyofthesingularmatrixvariatebetadistributionofrank1[1]tothecaseofageneralrank.Astherelateddistributiontonormalsampling,thematrixvariatebetadistribution(alsocalledthemultivariatebetadistribution)hasbeenstudiedextens...     

Singular‐value (and eigenvalue) distribution and Krylov preconditioning of sequences of sampling matrices approximating integral operators

Let k( ? , ? ) be a continuous kernel defined on Ω × Ω, Ω compact subset of , , and let us consider the integral operator from into ( set of continuous functions on Ω) defined as the map is a compact operator and therefore its spectrum forms a bounded sequence having zero as unique accumulation point. Here, we first consider in detail the approximation of by using rectangle formula in the case where Ω = [0,1], and the step is h = 1 ∕ n. The related linear application can be represented as a matrix A_n of size n. In accordance with the compact character of the continuous operator, we prove that {A_n} ～ _σ0 and {A_n} ～ _λ0, that is, the considered sequence has singular values and eigenvalues clustered at zero. Moreover, the cluster is strong in perfect analogy with the compactness of . Several generalizations are sketched, with special attention to the general case of pure sampling sequences, and few examples and numerical experiments are critically discussed, including the use of GMRES and preconditioned GMRES for large linear systems coming from the numerical approximation of integral equations of the form (1) with and datum g(x). Copyright © 2014 John Wiley & Sons, Ltd.     

Residues of codimension one singular holomorphic distributions

For a codimension one locally-free singular holomorphic distribution, we give a residue formula in terms of the conormal sheaf given by Pfaffian equations. We also prove a Baum-Bott type residue formula for singular distributions.      

Asymptotic spectra of large matrices coming from the symmetrization of Toeplitz structure functions and applications to preconditioning

The singular value distribution of the matrix‐sequence {Y_nT_n[f]}_n , with T_n[f] generated by $f\in L^1([?\pi ,\pi ])$, was shown in [J. Pestana and A.J. Wathen, SIAM J Matrix Anal Appl. 2015;36(1):273‐288]. The results on the spectral distribution of {Y_nT_n[f]}_n were obtained independently in [M. Mazza and J. Pestana, BIT, 59(2):463‐482, 2019] and [P. Ferrari, I. Furci, S. Hon, M.A. Mursaleen, and S. Serra‐Capizzano, SIAM J. Matrix Anal. Appl., 40(3):1066‐1086, 2019]. In the latter reference, the authors prove that {Y_nT_n[f]}_n is distributed in the eigenvalue sense as $${?}_{|f|}(\theta )=\left\{\begin{array}{ll}|f(\theta )|,& \theta \in [0,2\pi ],\\ ?|f(?\theta )|,& \theta \in [?2\pi ,0),\end{array}\right.$$ under the assumptions that f belongs to $L^1([?\pi ,\pi ])$ and has real Fourier coefficients. The purpose of this paper is to extend the latter result to matrix‐sequences of the form {h(T_n[f])}_n , where h is an analytic function. In particular, we provide the singular value distribution of the sequence {h(T_n[f])}_n , the eigenvalue distribution of the sequence {Y_nh(T_n[f])}_n , and the conditions on f and h for these distributions to hold. Finally, the implications of our findings are discussed, in terms of preconditioning and of fast solution methods for the related linear systems.     

A note on the spectral distribution of toeplitz matrices

An elementary and direct proof of the Szegö formula is given, for both eigen and singular values. This proof, which is based on tools from linear algebra and does not rely on the theory of Fourier series, simultaneously embraces multilevel Toeplitz matrices, block Toeplitz matrices and combinations of them. The assumptions on the generating

function f are as weak as possible; indeedf is a matrix-valued function of p variables, and it is only supposed to be integrable. In the case of singular values f(x), and hence the block p-level Toeplitz matrices it generates, are not even supposed to be square matrices. Moreover, in the asymptotic formulas for eigen and singular values the test functions involved are not required to have compact support.     

具分布时滞的广义系统周期解的存在性

研究了一类具分布时滞的广义系统模型,用矩阵测度和Krasnoselskii不动点定理获得了其周期解存在的若干充分条件,并举例说明其应用.     

条件风险下的两基金分离定理

鉴于条件风险价值CVaR具有风险度量的合理性以及两基金分离定理对证券投资的重要意义,以CVaR作为风险度量研究两基金分离定理.在组合收益率服从正态分布的假设下,分别就投资组合含有或没有无风险资产的情形提出并证明了两基金分离定理;放开方差-协方差矩阵为非奇异这一通常假设,证明了CVaR风险度量下的两基金分离定理依然成立.     

THE ASYMPTOTIC SOLUTION FOR A CLASS OF SINGULARLY PERTURBED PROBLEM FOR SEMILINEAR SINGULAR EQUATION

The singularly perturbed boundary value problem for a class of semilinearsingular equation is considered. Using a simple and special method the asym-ptotic behavior of solution is studied.     

Singular hyperbolicity for transitive attractors with singular points of 3-dimensional <Emphasis Type="Italic">C</Emphasis><Superscript>2</Superscript>-flows

In the context of C^r-flows on 3-manifolds (r ≥ 1), the notion of singular hyperbolicity, inspired on the Lorenz Attractor, is the right generalization of hyperbolicity (in the sense of Smale) for C¹-robustly transitive sets with singularities. We estabish conditions (on the associated linear Poincaré flow and on the nature of the singular set) under which a transitive attractor with singularities of a C²-flow on a 3-manifold is singular hyperbolic.     

H-矩阵的实用判定及谱分布

1引言及记号因为非奇异H-矩阵主对角元非零,所以本文总假定所涉及矩阵主对角元非零,并且设A=(aij)∈Cn×n为n阶复方阵,N={1,2,…,n}．记N1={i∈N |Pi(A)<|aii|Pi(A)}, N4={i∈N | |aii|≥Pi(A)>Ri(A)}, N5={i∈N | |aii|>Pi(A)=Ri(A)},N0={i∈N | |aii|≤Ri(A),|aii|≤Pi(A)},即N=N1∪N2∪N3∪N4∪N5∪N0．