样本协方差矩阵

词组短语

sample covariance matrix

与样本协方差矩阵相关的网络例句 [注：此内容来源于网络，仅供参考]

This thesis has mainly contributed two results in theory: one is that the covariance matrix is not positive definitive if the number of sampling is least than the number of variables, the other is that the covariance matrix of discrete sample is positive definitive if and only if the sum of every random column vector of data matrix is non I-linear combination.

本论文在理论上，主要得出两个新的结果：ⅰ抽样调查中，若样本的个数少于变量的个数则样本协方差矩阵恒为非正定；ⅱ离散型样本协方差矩阵正定的充要条件是样本资料阵的各随机列向量是非I-线性组合。

In a special case of 2 dimensional variables, the probability of the positive definitiveness of the discrete sample covariance matrix is given in term of sample size and variable dimension. Based on the results, the optimal sample size is provided in this thesis.

推出了离散型样本协方差矩阵正定的充要条件，得到了求离散型样本协方差矩阵正定概率的模型，建立了特殊情况下的抽样优化模型。

Studying the positive definitiveness of the covariance matrix from discrete data is important to design the sampling plan and provides the base for the data analysis.However, there have been few outcomes about the positive definitiveness of covariance matrix, most of which have been restricted to the Covariance-matrix of continuous sample.

研究离散型样本协方差矩阵的正定性有助于判断是否可以降低样本的维数，有利于优化抽样个数，为抽样调查的优化设计方案提供一定的理论基础；为基于特征根的多元统计分析提供理论指导。

Moreover, it processes only a single snapshot data with no use for the formation of the sample covariance matrix and matrix inversion operation.

同时，它只对单快拍数据进行处理，避免了样本协方差矩阵的构造以及矩阵求逆运算，更适合于实时处理。

The proposed method first uses common subtractive clustering locating method to acquire video object position and moving object number of each frame. Then, the proposed algorithm uses fuzzy C-means clustering algorithm to further cluster the moving object foreground pixel samples. Finally, matrix analysis theory for calculating eigenvalues and eigenvectors of covariance matrix is applied to compute the object scale and orientation parameters.

该算法在减法聚类算法预定位目标位置及获得目标个数的基础上，进一步采用模糊C均值聚类对目标前景样本进行归类，最后通过对目标前景样本协方差矩阵特征值和特征向量的分析获得目标的尺度及方向参数，从而实现对视频运动目标的定位。

Based on the previous fault possibility distribution kept within the sample mean and covariance matrix with forgetting factors,a strategy for constructing the target output of the new training sample set was ...

该算法通过带有遗忘因子的样本均值和样本协方差矩阵保存样本所包含的故障可能性分布信息，并在此基础上产生新增样本的目标输出，用于训练FBF网络，以实现故障分类边界的在线跟踪。

For these important and practical problems,sample covariance matrix is an important statistic because many important statistics are functionals of it.When we make statistical inferences,such as estimations and/or hypothesis tests,the sample covariance matrices must be investigated.

以上诸多方面在做估计和假设检验过程中，无不用到大维随机矩阵的处理，尤其是大维样本协方差矩阵的相关性质，因为多元分析中许多重要的统计量都可以表示成样本协方差矩阵的函数。

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。

Five ways on comparing covariance matrix are applied to the Shanghai 50 Indexes Stock Exchange, which are sample covariance matrix, scalar matrix, two-parameter covariance matrix, single index matrix, constant correlation matrix. We adopt principal components method and Markowitz portfolio method to measure stock market risk using VaR, getting the effect of measuring market risk. The result shows that sample covariance matrix and two-parameter covariance matrix could measure market risk more effectively.

本文以上证50指数数据为样本，采用样本协方差矩阵、数量矩阵、两参数模型矩阵、单指数模型矩阵、常量相关矩阵作为与股票相关的协方差矩阵，结合投资策略选择的主成分方法和Markowitz最优投资组合方法，计算VaR以度量市场风险，并比较了五种协方差矩阵度量市场风险的效果，结果表明，在主成分方法中，样本协方差矩阵和两参数矩阵方法能有效的度量市场风险。

Also, a traditional test method is proposed by the Bartletts decomposition of sample covariance matrices and the sample mean vectors.

另外本文还利用样本协方差矩阵的Bartlett分解和样本均值，给出了检验共同均值的一种传统方法，这种检验犯第一类错误的概率比标称的检验水平略低。

与样本协方差矩阵相关的网络解释 [注：此内容来源于网络，仅供参考]

sample correlation matrix：样本相关矩阵

sample correlation coefficient 样本相关系数 | sample correlation matrix 样本相关矩阵 | sample covariance 样本协方差

sample correlation matrix：样本相关矩阵,样本相关阵

sample correlation coefficient 样本相关系数 | sample correlation matrix 样本相关矩阵,样本相关阵 | sample covariance 样本协方差

sample covariance function：样本协方差函数

sample covariance coefficient 样本协方差系数 | sample covariance function 样本协方差函数 | sample covariance matrix 样本协方差矩阵

covariance matrix：协方差矩阵

在SEM分析中,研究者搜集N个样本对P个外显变项(observable variable)的反应,所得结果以一个(p p)协方差矩阵(covariance matrix)表示. 此矩阵包含了样本外显变项间的相互关系,称为样本协方差矩阵(sample covariance matrix),简写S.

sample covariance matrix：样本协方差矩阵

此矩阵包含了样本外显变项间的相互关系,称为样本协方差矩阵(sample covariance matrix),简写S. 为揭示外显变项相互关系所隐含潜伏因子(latent factors)之特性及关系,研究者建立模式界定潜伏因子与外显变项之关系,称为测量模式(measurement model).