Estimation of a Normal Covariance Matrix with Incomplete Data under Stein′s Loss

Suppose that we have (n − a) independent observations from N_p(0, Σ) and that, in addition, we have a independent observations available on the last (p − c) coordinates. Assuming that both observations are independent, we consider the problem of estimating Σ under the Stein′s loss function, and show that some estimators invariant under the permutation of the last (p − c) coordinates as well as under those of the first c coordinates are better than the minimax estimators of Eaten. The estimators considered outperform the maximum likelihood estimator (MLE) under the Stein′s loss function as well. The method involved here is computation of an unbiased estimate of the risk of an invariant estimator considered in this article. In addition we discuss its application to the problem of estimating a covariance matrix in a GMANOVA model since the estimation problem of the covariance matrix with extra data can be regarded as its canonical form.     

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.     

Bayesian Estimation of the Precision Matrix with Monotone Missing Data

Lithuanian Mathematical Journal - Abstract. This research paper stands for the estimation of the precision matrix of the normal matrix with monotone missing data. We explicitly provide maximum and...     

有缺失数据的条件独立正态母体中参数的最优同变估计

在缺失数据机制是可忽略的假设下,导出了有单调缺失数据的条件独立正态模型中协方差阵和精度阵的Cholesky分解的最大似然估计和无偏估计.通过引入一类特殊的变换群并在更广义的损失下,获得了其最优同变估计.这表明最大似然估计和无偏估计是非容许的.最后,通过数值模拟验证了相关结果的有效性.     

Non‐Monotone Space Decomposition Methods for Minimization Problems

Parallel space decomposition methods for the numerical treatment of unconstrained minimization problems are presented. For a special case of these methods described in [1] we extend classical line search methods for subspace optimization by non‐monotone strategies of [2]. For the convergence theory the concept of a generalized minimizing sequence is introduced in extension of a concept in [3].     

Estimation of a Multivariate Normal Covariance Matrix with Staircase Pattern Data

In this paper, we study the problem of estimating a multivariate normal covariance matrix with staircase pattern data. Two kinds of parameterizations in terms of the covariance matrix are used. One is Cholesky decomposition and another is Bartlett decomposition. Based on Cholesky decomposition of the covariance matrix, the closed form of the maximum likelihood estimator (MLE) of the covariance matrix is given. Using Bayesian method, we prove that the best equivariant estimator of the covariance matrix with respect to the special group related to Cholesky decomposition uniquely exists under the Stein loss. Consequently, the MLE of the covariance matrix is inadmissible under the Stein loss. Our method can also be applied to other invariant loss functions like the entropy loss and the symmetric loss. In addition, based on Bartlett decomposition of the covariance matrix, the Jeffreys prior and the reference prior of the covariance matrix with staircase pattern data are also obtained. Our reference prior is different from Berger and Yang’s reference prior. Interestingly, the Jeffreys prior with staircase pattern data is the same as that with complete data. The posterior properties are also investigated. Some simulation results are given for illustration.     

缺失数据下指数威布尔分布族参数的经验Bayes分析

本文讨论了缺失数据下指数威布尔分布族参数的经验Bayes（EB）检验问题,利用概率密度函数的核估计构造了参数的经验Bayes检验函数,并证明了所提出的经验Bayes检验函数的渐近最优（a．o．）性,并获得了它的收敛速度．     

Monotone approximations of Green's functions

We study the approximations of the Green's function $\mathbb{G}$ in a domain Ω obtained from an approximation of the Dirac mass ${\delta }_0$. We prove that under some conditions, these approximations converge monotonically to $\mathbb{G}$, a rather surprising result. To cite this article: E. Chasseigne, R. Ferreira, C. R. Acad. Sci. Paris, Ser. I 339 (2004).     

方差分量模型协方差阵的谱分解

本文对平衡方差分量模型, 给出了其协方差阵的新的谱分解算法. 该方法的特点是计算简单, 易于理解, 无须复杂的数学知识. 且能够明确显示协方差阵的不同特征值的个数, 以及谱分解中不同特征值所对应的投影阵的显式表示. 基于新方法我们进一步研究了平衡方差分量模型的一些相关性质.本文还研究了一般方差分量模型, 我们首先定义了一般方差分量模型协方差阵的简单谱分解,给出了一般方差分量模型可以进行简单谱分解的充要条件, 并研究了协方差阵简单谱分解的一些性质. 对于协方差阵可以进行简单谱分解的方差分量模型, 本文研究了简单谱分解在其统计推断中的应用.     

The Spectral Decomposition of Covariance Matrices for the Variance Components Models

The aim of this paper is to propose a simple method to determine the number of distinct eigenvalues and the spectral decomposition of covariance matrix for a variance components model. The method introduced in this paper is based on a partial ordering of symmetric matrix and relation matrix. A method is also given for checking straightforwardly whether these distinct eigenvalues are linear dependent as functions of variance components. Some examples and applications to illustrate the results are presented.     

Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution

It is well known that the best equivariant estimator of the variance covariance matrix of the multivariate normal distribution with respect to the full affine group of transformation is not even minimax. Some minimax estimators have been proposed. Here we treat this problem in the framework of a multivariate analysis of variance (MANOVA) model and give other classes of minimax estimators.     

Monotone Empirical Bayes Test for the Exponential Distribution

We study the two-action problem in the exponential distribution via empirical Bayes (EB) approach. Based on typeⅡcensored samples, we construct an EB test rule and obtain an optimal rate of convergence which much improves the existing results in the literature.     

截尾正态分布的最小后验风险Bayes推断

设有两个总体G0、G1,分别服从参数为(μ0,σ)与(μ1,σ)的截尾正态分布.基于寿命数据X,考虑判别问题μ=μ0 vs.μ=μ1(μ1＞μ0＞0).本文依据最小后验风险准则,给出了上述问题的Bayes判决方法,为寿命判别制定了一个简便操作的规则.     

Decomposition Method for a Class of Monotone Variational Inequality Problems

In the solution of the monotone variational inequality problem VI(, F), with

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本文研究了一种可用于分析非负数据的新的分布族. 它用一个随机表示来定义, 是一个半正态随机变量与一个指数随机变量的幂的混合. 其密度函数和性质, 包括风险函数, 矩和矩母函数被导出. 该分布族的实用性和灵活性通过一个实例用最大似然程序被阐明.     

Strassen′s Law of the Iterated Logarithm for the Lorenz Curves

Under some mild conditions we establish Strassen′s law of the iterated logarithm for the Lorenz curves.     

Efficient ML Estimation of the Multivariate Normal Distribution from Incomplete Data

It is well known that the maximum likelihood estimates (MLEs) of a multivariate normal distribution from incomplete data with a monotone pattern have closed-form expressions and that the MLEs from incomplete data with a general missing-data pattern can be obtained using the Expectation-Maximization (EM) algorithm. This article gives closed-form expressions, analogous to the extension of the Bartlett decomposition, for both the MLEs of the parameters and the associated Fisher information matrix from incomplete data with a monotone missing-data pattern. For MLEs of the parameters from incomplete data with a general missing-data pattern, we implement EM and Expectation-Constrained-Maximization-Either (ECME), by augmenting the observed data into a complete monotone sample. We also provide a numerical example, which shows that the monotone EM (MEM) and monotone ECME (MECME) algorithms converge much faster than the EM algorithm.     

Independence Distribution Preserving Covariance Structures for the Multivariate Linear Model

Consider the multivariate linear model for the random matrixY_n×pMN(XB, VΣ), whereBis the parameter matrix,Xis a model matrix, not necessarily of full rank, andVΣ is annp×nppositive-definite dispersion matrix. This paper presents sufficient conditions on the positive-definite matrixVsuch that the statistics for testingH₀: CB=0vsH_a: CB≠0have the same distribution as under the i.i.d. covariance structureIΣ.     

On the Complexity of Dualization of Monotone Disjunctive Normal Forms

We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inn^{o(log n)}time.