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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。

In contrast,the theory of large dimensional random matrices,especially,that of the large sample covariance matrices become a very significant and powerful mathematical topic.

因此大维随机矩阵理论,尤其协方差矩阵相关性质的研究就成为非常重要且迫切需要解决的课题。

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。

The paper studies the auto-covariance estimation of Gamma distribution by using the asymptotic behavior of student U -statistic,gives the large sample interval estimation for the scale parameter,and carries out the computing simulation according to the method of random simulation.

利用学生氏U-统计量的渐近性质,研究伽玛分布的自协方差估计,同时给出了尺度参数的大样本区间估计,并采用随机模拟的方法进行计算模拟

Covariance, correlation, linear combinations of random variables, sampling distribution of the mean, confidence intervals for means and proportions.

协方差,相关性,线性组合的随机变量,抽样分布的意思是说,置信区间的手段和比例。

Singular value decomposition of observability matrix is used to obtain an optimal 2-position alignment, the result is the same as that of covariance analysis.

采用奇异值分解方法对捷联惯导系统静基座下的多位置对准过程中最优位置的选择进行了分析,其结果与协方差分析的结果相同。

For the singular spectrum analysis of time series, a method of constructing covariance matrix by using forth order cumulant is proposed. The method could restrain the Gaossian noise and symmetrical distributed noise mixed with chaotic signals.

对于时间序列的高阶奇异谱分析,提出了一种利用四阶累积量函数构造协方差矩阵的方法,该方法从理论上能有效地抑制叠加在混沌信号中的高斯噪声和对称分布噪声对重构结果的影响。

Thereinto, for the spectral decomposition estimate of the covariance matrix ,we can gain the risk functions under some losses.

其中,对于观测向量协方差阵的谱分解估计,我们很容易得到它在一些损失下的风险函数。

A reversible Markov process as a chemical polymerization model where a gelation transition exists is presented. The stationary distribution, partition function and expression of covariance of this model are obtained.

以存在凝胶化现象的高分子反应模型为背景,定义了高分子反应所对应的可逆Markov过程,给出了过程的稳定分布及配分函数,推导出了分子间协方差的表达式。

In this dissertation , the methods of prediction and parameters\' estimation based on stationary multivariate autoregressive models AR(p are studied systematically. Some properties of these methods are formulized.In the first part, it introduce some basal conceptions about multidimensional stationary time series, contained the estimation and property of mean vector and auto-covariance function. Then the stable condition of multidimensional AR models are discussed.

本文主要研究了多维平稳序列自回归模型AR的参数估计及其相关的统计性质,具体内容如下:第一部分,介绍了多维平稳时间序列及其均值向量和协方差阵函数的概念,并引入了多维AR模型及其数字特征,在此基础上给出了模型的平稳性条件。

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