NS Condition of Admissibility for the Linear Estimator of Normal Mean with Unknown Variance

Suppose Y - N（β, σ^2 In）, where β ∈ R^n and σ^2 〉 0 are unknown. We study the admissibility of linear estimators of mean vector under a quadratic loss function. A necessary and sufficient condition of the admissible linear estimator is given.     

独立Poisson分布的均值之MLE的改进

In this paper, we consider the simultaneous estimation of the parameters (means) of the independent Poisson distribution by using the following loss functions: L0(θ,T)=∑i=1^n(Ti-θi)^2,L1(θ,T)=∑i=1^n(Ti-θi)^2/θi We develop an estimator which is better than the maximum likelihood estimator X simultaneously under L0(θ, T) and L1(θ, T). Our estimator possesses substantially smaller risk than the usual estimator X to estimate the parameters (means) of the independent Poisson distribution.     

<Emphasis Type="Italic">f</Emphasis>-Colorings of some graphs of <Emphasis Type="Italic">f</Emphasis>-class 1

An f-coloring of a graph G is an edge-coloring of G such that each color appears at each vertex v V（G） at most f（v） times. The minimum number of colors needed to f-color G is called the f-chromatic index of G and is denoted by X′f（G）. Any simple graph G has the f-chromatic index equal to △f（G） or △f（G） ＋ 1, where △f（G） =max v V（G）{[d（v）/f（v）]}. If X′f（G） = △f（G）, then G is of f-class 1; otherwise G is of f-class 2. In this paper, a class of graphs of f-class 1 are obtained by a constructive proof. As a result, f-colorings of these graphs with △f（G） colors are given.     

A New Class of Complementarity Function and the Boundedness of Its Merit Function for Symmetric Cone Complementarity Problem

In this paper, we introduce a new class of two-parametric penalized function, which includes the penalized minimum function and the penalized Fischer-Burmeister flmc- tion over symmetric cone complementarity problems. We propose that this class of function is a class of complementarity functions（C-function）. Moreover, its merit function has bounded level set under a weak condition.     

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.     

LINEX损失函数下的信度模型(英文)

LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.     

GAMMA-MINIMAX ESTIMATORS FOR THE MEAN VECTOR OF A MULTIVARIATE NORMAL DISTRIBUTION

Γ-minimax estimators are determined for the mean vector of a multivariate normaldistribution under arbitrary squared error loss.Thereby the set Γ consists of all priorswhose vector of first moments and matrix of second moments satisfy some given restric-tions.Necessary and sufficient conditions are derived which ensure a prior being leastfavourable in Γ and the unique Bayes estimator with respect to this prior being Γ-minimax.By applying these results the Γ-minimax estimator is explicitly found in some special casesor can be computed by solving a system of non-linear equations or by minimizing a quad-ratic form on a compact and convex set.     

The Asymptotically Optimal Empirical Bayes Estimation about a Class of Uniform Distribution

In this note, we propose a squared error loss empirical Bayes estimator of θ based on past experiences and a present observation X which has conditional distribution. U(θ, cθ+b), where b is an arbitary constant when c>1; b>c when c=1, θ∈Ω=(-b/c-1,∞). When unkown prior G(θ) of θ belongs to the family {G:integral from Ω (θ~2dG(θ)<∞)}, our estimator is asymptotically optimal (see [1]). Let K(x) and k(x) be marginal distribution and density of r. v. X. It is easily seen that     

Hölder continuity for a class of X-elliptic equations with singular lower order term

The regularity for a class of X-elliptic equations with lower order term
Lu＋vu=-∑i,j=1 mXj^＊（aij（x）Xiu）＋vu=μ
is studied, where X = {X1,..., Xm} is a family of locally Lipschitz continuous vector fields, v is in certain Morrey type space and μ a nonnegative Radon measure. The HSlder continuity of the solution is proved when μ satisfies suitable growth condition, and a converse result on the estimate of μ is obtained when u is in certain HSlder class.     

Admissibility of Linear Estimators in the Growth Curve Model with Respect to Inequalitv Restriction

For the growth curve model with respect to inequality restriction： Y = XBZ ＋ε,ε（0, σ2V I）, trNB ≥0, this paper gives some necessary and sufficient conditions for the linear estimator of KBL to be admissible in the class of homogeneous linear estimators LH and nonhomogeneous linear estimators LI, respectively, under the quadratic loss function tr（d（Y） - KBL）＇（d（Y） - KBL）.     

Estimation of the entropy of a multivariate normal distribution

Motivated by problems in molecular biosciences wherein the evaluation of entropy of a molecular system is important for understanding its thermodynamic properties, we consider the efficient estimation of entropy of a multivariate normal distribution having unknown mean vector and covariance matrix. Based on a random sample, we discuss the problem of estimating the entropy under the quadratic loss function. The best affine equivariant estimator is obtained and, interestingly, it also turns out to be an unbiased estimator and a generalized Bayes estimator. It is established that the best affine equivariant estimator is admissible in the class of estimators that depend on the determinant of the sample covariance matrix alone. The risk improvements of the best affine equivariant estimator over the maximum likelihood estimator (an estimator commonly used in molecular sciences) are obtained numerically and are found to be substantial in higher dimensions, which is commonly the case for atomic coordinates in macromolecules such as proteins. We further establish that even the best affine equivariant estimator is inadmissible and obtain Stein-type and Brewster–Zidek-type estimators dominating it. The Brewster–Zidek-type estimator is shown to be generalized Bayes.     

加权p,q对称熵损失下一类指数分布模型参数的估计

在由信息论中的熵演绎出的一种新损失一加权P,q对称熵损失L(θ,δ)=θ/Pδp+δq/qθq-2(ρ,q>0)下,研究了一类指数分布模型c(x,η)θ-νe-νe-T(x)/θ的参数θ的Bayes估计的一般形式与精确形式,讨论了参数θ的形如cT(X)+d的一类估计的可容许性与不可容许性,并应用积分变换定理证明了参数θ的Bayes估计与可容许估计具有不变性,     

一种对称损失函数下一类指数分布族刻度参数的估计

在p,q对称熵损失函数L(θ,δ)=θp/δp+δq/θq-2(p,q0)下,研究了一类指数分布族c(x,n)θ-ve-T(x)/θ的刻度参数θ的Bayes估计与可容许估计,并应用积分变换定理证明了这两个估计具有不变性.     

UNIFORMLY A POSTERIORI ERROR ESTIMATE FOR THE FINITE ELEMENT METHOD TO A MODEL PARAMETER DEPENDENT PROBLEM

This paper proposes a reliable and efficient a posteriori error estimator for the finite element methods for the beam problem. It is proved that the error can be bounded by the computable error estimator from above and below up to multiplicative constants that do neither depend on the meshsize nor on the thickness of the beam.     

A POSTERIORI ESTIMATOR OF NONCONFORMING FINITE ELEMENT METHOD FOR FOURTH ORDER ELLIPTIC PERTURBATION PROBLEMS

In this paper, we consider the nonconforming finite element approximations of fourth order elliptic perturbation problems in two dimensions. We present an a posteriori error estimator under certain conditions, and give an h-version adaptive algorithm based on the error estimation. The local behavior of the estimator is analyzed as well. This estimator works for several nonconforming methods, such as the modified Morley method and the modified Zienkiewicz method, and under some assumptions, it is an optimal one. Numerical examples are reported, with a linear stationary Cahn-HiUiard-type equation as a model problem.     

Admissibility and minimaxity of Bayes estimators for a normal mean matrix

In some invariant estimation problems under a group, the Bayes estimator against an invariant prior has equivariance as well. This is useful notably for evaluating the frequentist risk of the Bayes estimator. This paper addresses the problem of estimating a matrix of means in normal distributions relative to quadratic loss. It is shown that a matricial shrinkage Bayes estimator against an orthogonally invariant hierarchical prior is admissible and minimax by means of equivariance. The analytical improvement upon every over-shrinkage equivariant estimator is also considered and this paper justifies the corresponding positive-part estimator preserving the order of the sample singular values.     

熵损失函数下的信度模型

本文研究了信度模型问题.利用熵损失函数,获得了风险保费的信度估计和经验Bayes信度估计.所获结果是对现有风险保费信度估计和经验Bayes信度估计的一个补充.     

矩阵损失函数下参数估计的几个结果

在本文中,设随机向量 Y 的样本空间和分布族为((?)P_θ),θ∈(?),(?)为 p 维欧氏空间 R~p 中的 Borel 集.要估计θ的函数的向量 h(θ)=(h_1(θ),…,h_k(θ))'.文献[1]中第二章的定理1.4指出,若存在 h(θ)的无偏估计δ(Y),使得 E_θ(δ(Y)—h(θ))′(δ(Y)—h(θ))<∞,一切θ∈(?),则在损失函数(α—h(θ))′(α—h(θ))下,(?)(Y)是 h(θ)的一致最优无偏估计的充要条件是对 h(θ)的任何风险函数有限的无偏估     

一种对称损失函数下刻度参数的最优同变估计

本文研究刻度参数分布族(1/σ)f(x/σ)中刻度参数在损失函数L(σ,δ)=(σ-δ)~2/σδ下的最小风险同变估计及其最小最大性。     

带有结构变化的线性模型中参数估计的一些结果

本文在一些纯量损失和矩阵损失下研究带有结构变化的正态线性模型中参数的估计问题．分别给出 了存在回归系数的一致最小风险无偏（ＵＭＲＵ）估计和一致最小风险同变（ＵＭＲＥ）估计的充要条件, 证明了不存在误差方差在仿射变换群下的ＵＭＲＥ估计．导出了回归系数的最小二乘估计的可容许性 和极小极大性．