样本协方差

We discuss asymptotically best linear unbiased estimators of the Logistic population based on selected order statistics and give the formula of computing the ABLUE, the variance and the covariance of the ABLUE in limit, and properties of the ABLUE, and so on. We give the optimum chosen of spacing which is the ABLUE with maximum asymptotic relative efficiency based on complete sample when selected order statistics number is less than 10, and obtain its maximum asymptotic relative efficiency.

讨论基于Logistic总体Ⅱ型截尾样本的若干个样本分位数的总体分布参数的近似最佳线性无偏估计；给出了该近似最佳线性无偏估计的计算公式，估计方差与协方差的极限表达式及估计量的大样本性质等；给出了全样本，样本分位点个数固定，但不超过10情形下，使上述近似最佳线性无偏估计有最大相对联合估计效率时，样本分位数选取法及最大相对联合估计效率；为大样本情形下，该近似最佳线性无偏估计的应用提供了理论依据。

Events,Operation and Relation of Sets, Classical Probability, Geometrical Probability , Statistical Stability of a Frequency, Axioms of Probability, Conditional Probability, Total Probability Theorem, Bayes' Rule,Independent Events,Independent Repeated Trials, One Dimensional Random Variables, Discrete Random Variables, Distribution Function of a Random Variables , Continuous Random Variables, Normal Distribution, Distribution of a Function of a Random Variable, Multidimensional Random Variables, Joint Distribution Function, Marginal Distribution Function,Discrete Two—Dimensional Random Variables,Continuous Two—Dimensional Random Variables, Independent Random Variables, Distribution of Functions of Random Variables,Expectation,Variance, Covariance, Coefficient of Correlation, Bivariate Normal Distribution, Law of Large Numbers, The Central Limit Theorems, Sample and Population ,Chi—Squared, T and F Distributions , Sampling Distributions , Point Estimation , Interval Estimation , Testing Hypotheses , A Test of Significance for Parameters in a Single Sample From a Normally Distributed Population , A Test of Significance for Parameters in Two Sample From Normally Distributed Populations .

本课程的主要内容：概率的概念与运算、随机变量及其分布、随机变量的数字特征与极限定理、数理统计的基本概念、估计和检验的基本方法，随机事件与概率随机事件、事件的关系与运算、几何概率、统计概率等，条件概率、全概率公式、贝叶斯公式、事件的独立性、二项概率公式，随机变量的概念、离散型随机变量、随机变量的分布函数、连续型随机变量、随机变量函数的分布，多维随机变量及其分布函数、边缘分布函数、随机变量的独立性、二维随机变量函数的分布，数学期望、方差、协方差和相关系数、大数定律、中心极限定理，总体与样本， X 2-分布、 t-分布和 F-分布，统计量及抽样分布，假设检验的基本概念、单个正态总体参数的显著性检验、两个正态总体参数的显著性检验。

The paper consists of three parts. First, we give the calculation for every order central moment for the Logistic population, mean vector and covariance matrix of the order statistic of random sample from the Logistic population, and means of several special functions for the order statistic of random sample from the Logistic population. Secondly, we discuss the parameter estimation for the Logistic population. Finally, we solve the question about the goodness-of -fit test for the Logistic population.

研究的主要内容分三部分：第一部分是关于Logistic总体各阶中心矩、样本次序统计量期望向量与协方差阵及若干个特殊的样本次序统计量函数期望的计算问题；第二部分是关于Logistic总体分布参数的估计问题；第三部分是关于Logistic总体的拟合优度检验问题。

Traditional multivariable analysis method, for example, principal component analysis method and factor analysis method, are common in calculating the mean vector, the covariance matrix of sample and other variables.

摘要传统的多元统计分析方法，如主成分分析方法和因子分析方法等的共同点是计算样本的均值向量和协方差矩阵，并在这两者的基础上计算其他统计量。

The premise of the superiority of the conventional statistical STAP is to have ample training samples with interference that are independently and identically distributed with interference in the test sample, in order to estimate the covariance matrix.

常规的统计STAP方法性能最优的前提条件是具有足够的与待检测样本中的干扰独立同分布的训练样本，以便估计协方差矩阵。

A new approach-two dimensional principal component analysis(2DPCA) is developed for face recognition. 2DPCA approach dont need to transform the image matrix into feature vector. It directly uses the image matrix to construct the sample covariance matrix.

5研究了二维主成分分析(2DPCA)方法，2DPCA方法不需要先将图像矩阵展开成一维向量，而是直接利用图像矩阵来构建样本协方差矩阵，所以2DPCA比PCA的识别时间更短，识别更高。

The slope estimate is the sample covariance between x and y divided by the sample variance of x.

斜率估计量等于样本中x和 y 的协方差除以x的方差。若x和 y 正相关则斜率为正，反之为负。

The improved criteria on the constraint conditions of kernel functions in our results should also provide a better understanding of the asymptotic properties of the ESD of large dimensional sample covariance matrices.

具我们结果中核函数的限制的改进准则，可以对大维样本协方差矩阵的经验谱分布的渐近性质有一个更进一步的理解。

In the case of 〓unknown,this dissertation derived the exact distribution of related statistics,and thenproposed the multivariate covariance control charts:the A chart and the 〓 chart based onthe sample generalized variance and the maximum and the minimum eigenvalues.

当分布参数〓未知时，本文通过推导有关统计量的精确分布，建立了基于样本广义方差和最大、最小特征根的多元协方差控制图—A图和〓图，为实际应用奠定了基础。

The performance of a disturbance covariance matrix is degradated in space time adaptive processing when it is estimated with secondary data contaminated by targetlike signals.

针对空时自适应处理中样本协方差矩阵受干扰目标污染时检测性能下降的问题，提出了一种基于知识的空时自适应处理（knowledge aided space time adaptive processing， KASTAP）方法。

And Pharaoh spoke to Joseph, saying, Your father and your brothers have come to you.

47：5 法老对约瑟说，你父亲和你弟兄们到你这里来了。

Additionally, the approximate flattening of surface strip using lines linking midpoints on perpendicular lines between geodesic curves and the unconditional extreme value method are discussed.

提出了用测地线方程、曲面上两点间短程线来计算膜结构曲面测地线的方法，同时，采用测地线间垂线的中点连线和用无约束极值法进行空间条状曲面近似展开的分析。