半序约束下多维正态总体均值和协方差阵的最大似然估计

对给定K个P维正态总体,未知均值和协方差阵分别为θi和Λi,i=1,2,…,k,本文考虑均值和协方差阵之间都在一个简单半序约束θ1≤θ2≤…≤θk,Λ1≥Λ2≥…≥Λk>0条件下的估计问题.讨论θi和Λi的最大似然估计的性质,并给出一个求解的迭代方法.     

Some Simple Test Procedures for Normal Mean Vector with Incomplete Data

The problem of testing normal mean vector when the observations are missing from subsets of components is considered. For a data matrix with a monotone pattern, three simple exact tests are proposed as alternatives to the traditional likelihood ratio test. Numerical power comparisons between the proposed tests and the likelihood ratio test suggest that one of the proposed tests is indeed comparable to the likelihood ratio test and the other two tests perform better than the likelihood ratio test over a part of the parameter space. The results are extended to a nonmonotone pattern and illustrated using an example.     

ML Estimation of the MultivariatetDistribution and the EM Algorithm

Maximum likelihood estimation of the multivariatetdistribution, especially with unknown degrees of freedom, has been an interesting topic in the development of the EM algorithm. After a brief review of the EM algorithm and its application to finding the maximum likelihood estimates of the parameters of thetdistribution, this paper provides new versions of the ECME algorithm for maximum likelihood estimation of the multivariatetdistribution from data with possibly missing values. The results show that the new versions of the ECME algorithm converge faster than the previous procedures. Most important, the idea of this new implementation is quite general and useful for the development of the EM algorithm. Comparisons of different methods based on two datasets are presented.     

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.     

The Estimation of Parameters for the Tapered Pareto Distribution from Incomplete Data

Lithuanian Mathematical Journal - In this paper, we consider estimation of unknown parameters of the tapered Pareto distribution, which belongs to the class of semiheavy distributions, by a sample...     

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??Hidden Markov model is widely used in statistical modeling of time, space and state transition data. The definition of hidden Markov multivariate normal distribution is given. The principle of using cluster analysis to determine the hidden state of observed variables is introduced. The maximum likelihood estimator of the unknown parameters in the model is derived. The simulated observation data set is used to test the estimation effect and stability of the method. The characteristic is simple classical statistical inference such as cluster analysis and maximum likelihood estimation. The method solves the parameter estimation problem of complex statistical models.     

Maximum Likelihood Estimation of Hidden Markov Multivariate Normal Distribution Parameters

Hidden Markov model is widely used in statistical modeling of time, space and state transition data. The definition of hidden Markov multivariate normal distribution is given. The principle of using cluster analysis to determine the hidden state of observed variables is introduced. The maximum likelihood estimator of the unknown parameters in the model is derived. The simulated observation data set is used to test the estimation effect and stability of the method. The characteristic is simple classical statistical inference such as cluster analysis and maximum likelihood estimation. The method solves the parameter estimation problem of complex statistical models.     

多元$t$分布数据的局部影响分析

对于多元$t$分布数据, 直接应用其概率密度进行影响分析是困难的\bd 本文通过引入服从Gamma分布的权重, 将其表示为特定多元正态分布的混合\bd 在此基础上, 进而将权重视为缺失数据, 引入EM算法; 从而利用基于完全数据似然函数的条件期望进行局部影响分析\bd 本文进一步系统研究了加权扰动模型下的局部影响分析, 得到了相应的诊断统计量; 并通过两个实例说明了这种方法的有效性.     

Bartlett分解与多元正态总体均值的广义推断

对多个正态总体均值的统计推断是一个古老而令人感兴趣的问题.本文利用样本协方差矩阵的Bartlett分解和广义p-值的概念给出一些关于均值的精确检验.模拟显示这些检验比已有的检验有更高的功效.同时,还根据协方差矩阵的Bartlett分解和样本均值向量,得到一个分布和未知参数无关的统计量,用它可以对多个正态总体共同均值做精确检验.模拟显示,这些检验犯第一错误的概率小于显著性水平,而且有更高的检验功效.     

Estimation of the Multivariate Normal Precision Matrix under the Entropy Loss

Let X ₁, , X _n (n > p) be a random sample from multivariate normal distribution N _p (, ), where R ^p and is a positive definite matrix, both and being unknown. We consider the problem of estimating the precision matrix ^–1. In this paper it is shown that for the entropy loss, the best lower-triangular affine equivariant minimax estimator of ^–1 is inadmissible and an improved estimator is explicitly constructed. Note that our improved estimator is obtained from the class of lower-triangular scale equivariant estimators.     

多元正态向量均值变点在线监测

在这篇文章中, 我们提出了监测多元独立向量均值变点的两种方法. 第一种方法是构造基于残差的CUSUM统计量; 第二种方法是构造基于递归残差的CUSUM统计量. 并分别得到了对应统计量的渐进分布.模拟表明本文提出的方法监测效果良好, 生产实例也说明该方法具有一定的实用性和有效性.     

多元线性回归在分析学生成绩相关性中的应用

利用多元线性回归分析法,根据学生专业课成绩与基础课成绩的相关性,建立了回归方程,进行定量分析,结果为教学研究和管理提供了科学的依据.     

关于N（α，B）一个特性的初等演证

用较初等的方法演证了多元正态分布的一个特性,即N（α,B）可由其前二阶矩完全确定．     

单个多元正态总体均值向量联合置信区间的比较

单个多元正态总体均值向量的Bonferroni和Scheff\'{e}联合置信区间在实际中经常用到,本文主要采用解析的办法比较这两个置信区间的长短, 证明了当均值向量的维数$2\le p\le 12$时, Bonferroni联合置信区间比Scheff\'{e}联合置信区间短.     

Fixed Width Confidence Region for the Mean of a Multivariate Normal Distribution

Srivastava gave an asymptotically efficient and consistent sequential procedure to obtain a fixed-width confidence region for the mean vector of any p-dimensional random vector with finite second moments. For normally distributed random vectors, Srivastava and Bhargava showed that the specified coverage probability is attained independent of the width, the mean vector, and the covariance matrix by taking a finite number of observations over and above T prescribed by the sequential rule. However, the problem of showing that E(T−n₀) is bounded, where n₀ is the sample size required if the covariance matrix were known, has not been available. In this paper, we not only show that it is bounded but obtain sharper estimates of E(T) on the lines of Woodroofe. We also give an asymptotic expansion of the coverage probability. Similar results have recently been obtained by Nagao under the assumption that the covariance matrix Σ=∑^k_i=1 σ_iA_i and ∑^k_i=1 A_i=I, where A_i's are known matrices. We obtain the results of this paper under the sole assumption that the largest latent root of Σ is simple.     

The 0 ,1 - Distribution of the First CoordinateSequences Derived from ML- Sequences over Z/(2~e)

§1.　IntroductionThereseachindetailonlinearrecurringsequencesovertheresideclassringZ/(pe)start-edfromWardworkin30s(see[1]).Inthepastfewyears,fromthepointofviewofcrypto-graphandcodetheory,peopleisinterestedparticularlyinstudyingthecoordinatesequencesde-rivedfromML-sequencesoverZ/(pe),asnon-linearrecurringsequencesoverFp(see[2],[3]).SomeanalogousresultsextendedtoGaliosringcanbefoundin[4],[5].Butthereisnobetterresultontheresearchofthe0,1-distributionproperties.Fromavastamountofcalcula-tion,t…     

正态分布参数函数的最小最大估计

The estimation of the functionθ=exp{αμ bσ2} of parameters (μ,σ2) in normal distribution N(μ,σ2) is discussed. And when the prior distributions ofμandσ2 are independent, under the loss function L(θ,δ)=(θ-1×δ-1)2, the Bayesian estimation and the existence and computing method on minimax estimation are deeply discussed.     

Effects of Smoothing on Multivariate Statistical Properties of the Normal to a Rough Curve or Surface

Much of the practical interest attached to curves and surfaces derives from features of roughness, rather than smoothness. For example, considerable attention has been paid to fractal models of curves and surfaces, for which the notions of a normal and curvature are usually not well defined. Nevertheless, these quantities are sometimes measurable, because the device for recording a rough surface (such as a stylus or “compass”) adds its own intrinsic smoothness. In this paper we address the effect of such smoothing operations on the multivariate statistical properties of a normal to the surface. Particular attention is paid to the validity of commonly assumed unimodal approximations to the distribution of the normal. It is shown that the actual distribution may have more than one mode, although in a range of situations the unimodal approximation is valid.     

Identification of the Parameters of a Multivariate Normal Vector by the Distribution of the Maximum

This paper continues the work started by Basu and Ghosh (J. Mult. Anal. (1978), 8, 413–429), by Gilliland and Hannan (J. Amer. Stat. Assoc. (1980), 75, No. 371, 651–654), and then continued on by Mukherjea and Stephens (Prob. Theory and Rel. Fields (1990), 84, 289–296), and Elnaggar and Mukherjea (J. Stat. Planning and Inference (1990), 78, 23–37). Let (X₁, X₂,..., X_n) be a multivariate normal vector with zero means, a common correlation and variances ² ₁, ² ₂,..., ² _n such that the parameters , ² ₁, ² ₂,..., s² _n are unknown, but the distribution of the max{X_i: 1in} (or equivalently, the distribution of the min{X_i: 1in}) is known. The problem is whether the parameters are identifiable and then how to determine the (unknown) parameters in terms of the distribution of the maximum (or its density). Here, we solve this problem for general n. Earlier, this problem was considered only for n3. Identifiability problems in related contexts were considered earlier by numerous authors including: T. W. Anderson and S. G. Ghurye, A. A. Tsiatis, H. A. David, S. M. Berman, A. Nadas, and many others. We also consider here the case where the X_i's have a common covariance instead of a common correlation.     

Mixed Estimations and Bayes Estimation of theRegression Coefficient in Multivariate Linear Model

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