多元时间序列模型协方差矩阵的递归算法

本文推导了多元时序模型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法     

多元时间序列模型协方差矩阵的递归算法

本推导了多元时序横型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法。     

反循环矩阵的逆矩阵

贵刊1986年第10期刊出“循环矩阵的逆矩阵”。姚存峰作。(以下简称文[1]),看到这个结论使我们很自然地会想到,能否也给出     

关于Fuzzy矩阵的Moore-Penrose逆

讨论Fuzzy矩阵的Moore-PenrOSe逆,给出一些Moore-Penrose逆存在的充要条件以及Moore-Penrose逆的划画。     

某些分块矩阵的逆矩阵

本文研究了某些 3× 3分块矩阵的可逆性条件 ,并给出了可逆时的求逆公式     

关于环上矩阵的群逆与Drazin逆

本文给出了环上一类方阵有群逆，｛１，５｝－道的充要条件及其它们的表式，推广了体（域）上关于群逆的Ｃｌｉｎｅ定理．此外还首次得到了矩阵有Ｄｒａｚｉｎ逆的判别准则及其它的表式．     

协方差矩阵的齐性检验

基于ＰＰ技术、Ｂｏｏｔｓｔｒａｐ方法和数论方法,对于ｋ个总体协方差矩阵相等的检验,给出了ＰＰ型检验统计量,并讨论了它的渐近分布和Ｂｏｏｔｓｔｒａｐ逼近,最后给出了一些实际模拟结果。     

“杨辉三角”中的矩阵的逆矩阵

《数学通报》一九八八年五期载文《“杨辉三角”中的行列式》。该文对这种特殊的行列式给出了快速求值法,阅后颇有启发。本文将对“杨辉三角”中的矩阵探讨一种快速求逆法。“杨辉三角”系指     

关于环上矩阵的广义逆

本文得到了一般带有对合反自同构的结合环上一类矩阵{1,…,i}-逆存在的充要条件,给出了{3}-逆,{4}-逆,{1,…,i}-逆的表式,而这类矩阵则概括了左右主理想整环,单Artin环(特别是体)上所有矩阵.     

ADMISSIBLE ESTIMATES IN THE IMPORTANT CLASS OF ESTIMATES OF THE COVARIANCE MATRIX

Let X1,…XN(where N>m)be independent Nm(μ,∑）random vectors,and put X^-=1/N ∑i=1^N Xi and T‘T=A=∑i=1^N(Xi-X^-)(Xi-X^-)‘,where T is upper-triangular with positive diagonal elements.The author considers the problem of estimating ∑,and restricts his attention to the class of estimates D={T‘△^*T+Nb^*X^-X^‘·△^* is any diagonal matrix and b^* is any nonnegative constant}because it has the following attractive features:(a)Its elements are all quadratic forms of the sufficient and complete statistics(X^-,T).(b)It contains all estimates of the form αA+NbX^-X^-‘(α≥0 and b≥0),which construct a complete subclass of the class of nonnegative quadratic estimates D^8={X‘BX:B≥0}(where X=(X1,…,XN)‘)for any strict convex loss function.(c)It contains all invariant estimates under the transformation group of upper-triangular matrices.The author obtains the characteristics for an estimate of the form.T‘△T+NbX^-X^-‘(△=diag{δ1,…，δm}≥0 and b≥0)of ∑ to be admissible in D when the loss function is chosen as tr(∑^-1∑-I)^2,and shows,by an example,that αA+NX^-X^-‘(α≥0 and b≥0)is admissible in D^* can not imply its admissibility in D.     

IMPROVED ESTIMATES OF THE COVARIANCE MATRIX IN GENERAL LINEAR MIXED MODELS

In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.     

ON THE STATIONARITY AND INVERTIBILITY OF A GENERAL BILINEAR TIME SERIES MODEL

In this paper, some statistical properties of a general bilinear time series model BL (n,m,p,q) are discussed. The sufficient conditions for asymptotic stationarity and invertibility are derived and the expressions of the autocovariance are also given for this model. Results of derivation show that the expressions of autocovariance for a bilinear model are quite different to that of a linear model in the case of q≥ 2 .     

THE NECESSARY AND SUFFICIENT CONDITIONS FOR THE SOLVABILITY OF A CLASS OF THE MATRIX INVERSE PROBLEM

Censider the solutions of the matrix inverse problem, which are symmetric positive semide finite on a subspace. Necessary and sufficient conditions for the solvability, as well as the general solution are obtained. The best approximate solution by the above solution set is given. Thus the open problem in [1] is solved.     

子空间上亚半正定矩阵反问题解存在的条件

1 引 言 本文用R~(m×n)表示全体m×n阶实矩阵的集合,R~n为所有n维列向量的全体,OR~(n×n)为n阶正交矩阵的集合,I_n为n阶单位矩阵,A~T,A~ ,B(A),R(A)~⊥,N(A)分别表示矩阵A的转置,Moore-Penrose广义逆,值域,值域的正交补空间及零空间,Ps是     

二阶线性矩阵微分系统的振动性

讨论了二阶线性矩阵微分系统(P(t)Y′(t))′+Q(t)Y(t)=0,t≥t0,其中P(t),Q(t) 和Y(t)是n×n实连续矩阵函数, P(t)和Q(t)是对称的且P(t)>0是正定矩阵．利用推广的Riccati变换,采用两种不同的方法,得到了该系统振动的若干判据．所得结果推广和改进了已知的相应结果．     

随机环境中r维分支链的协方差计算

定义了各种条件多元概率母函数，并利用条件多元概率母函数这一强有力工具研究随机环境中r-维分支链的性质，并给出了其协方差阵的精确计算公式．     

标准Jacobi矩阵的混合型特征反问题

0 引言 本文讨论如下标准形式的Jacobi矩阵 其中a_i>0(i=1,2,…,n),b_i>0(i=1,2,…,n-1)。 对于Jacobi矩阵(对称三对角矩阵)的特征反问题,已有的成果[1],基本上集中在由两组频谱或两个特征对(指特征值及相应的特征向量)构造Jacobi矩阵的元素这样两类问题上,习惯上称之为频谱型或特征向量型反问题。本文提出且求解了第三类型——混合型特征反问题。即由一组频谱数据和一个特征向量构造矩阵元素的问题: 问题Ⅰ 给定正数λ~(1),λ~(2),…,λ~(n)和实向量x=(x_1,x_2,…,x_n)~T,其中x_1=1。构造一个标准形式的Jacobi矩阵J,使其第k阶顺序主子阵恰以λ~(k)(k=1,2,…,n)为其特征值。且(λ~(n),x)为其特征对。 问题Ⅱ 给定正数0<λ_1~(n)<λ_1~(n-1)<…<λ_1~(1)和正向量x=(x_1,x_2,…,x_n),其中x_=,x_k>0(k=2,…,n),构造一个标准形式的Jacobi矩阵J,使其第K阶顺序主子阵恰以λ_1~(k)为其最小特征值,而(λ~(n),x)为J的特征对。 问题Ⅲ 给定n个实数0<λ_1)<λ_2<…<λ_n和m个实数λ~(1),λ~(2),…,λ~(m)及m维向量x=(x_1,…,x_m)~T。构造n阶标准形式的Jaeobi矩阵J,使其第K阶顺序主子阵恰以λ~(k)(k=1,2,…,m)为其特征值,而(λ~(m),x)为第m阶顺序主子阵的特征对,且λ_k(k=1,2,…,n)为J的特征值。这里系大于或等     

关于两指标ARMA过程的谱密度

考察了两指标ＡＲＭＡ过程的模型与其谱密度之间的关系。     

A NOTE ON THE DRAZIN INVERSE OF A MODIFIED MATRIX

In this article,the expression for the Drazin inverse of a modified matrix is considered and some interesting results are established.This contributes to certain recent results obtained by Y.Wei [9].