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covariance相关的网络例句

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与 covariance 相关的网络例句 [注:此内容来源于网络,仅供参考]

In this paper, we get the distribution of the Bartletts decomposition of sample covariance matrices.

而把协方差矩阵当作相同的来处理,又会带来偏差。

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 sample covariance between each independent variable and the OSL residuals is zero.

每个自变量和OLS协残差之间的样本协方差为零。

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

In terms of covariance analysis, the elliptical splat of sample point is then determined on its tangent plane.

针对点模型的快速高质量绘制问题,提出一种单遍绘制算法。

Estimation of parameters for general liner functional model with singular covariance. First, the Rao decomposition of the sample variance and the known singular measure error variance are presented and then the square measure error with singular variance and the standardized condition are got.

首先讨论了样本协方差与已知的奇异度量误差方差的Rao分解,给出了含有奇异方差广义逆的二次度量误差和模型的标准化条件,证明了二次度量误差与广义逆的选取无关。

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。

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