协方差

基本解释 (translations)

covariance

与协方差相关的网络例句 [注：此内容来源于网络，仅供参考]

This section simply introduces the basic principles of the conventional analysis of time series (mean, variance, standard deviation; covariance and correlation coefficient; autocovariance and autocorrelation coefficient), frequency spectrum analysis and wavelet analysis.

本文简单地介绍了传统时序分析（包括平均值、方差和标准差；协方差和相关因子；自协方差和自相关因子）、频谱分析和小波分析的基本原理。

Four new relative efficiencies of the best linear unbiased estimator to the covariance improvement estimator for the unknown parameter vector are given, and three new relative efficiencies of the least square estimator to the covariance improvement estimator for the unknow parameter vector are given.

本文考虑相依模型，对其未知参数向量给出了其最佳线性无偏估计相对于协方差改进估计的四种相对效率，同时，还给出了最小二乘估计相对于协方差改进估计的三种相对效率，在不同条件下，分别给出了相对效率的上界与下界。

Collocation needs to know the covariance matrix of the signal vector,which is usually evaluated by a chosen covariance function and corresponding coefficient fitting.

拟合推估解算必须首先求得信号向量的方差协方差矩阵，该协方差矩阵一般通过选定的协方差函数，并通过已测点数据进行拟合得到。

The paper focuses on the multivariate Laplacian distribution. Based on MLD, we have obtained the variance of the quadric form, the covariance of two quadric forms and the covariance of the linear function and the quadric form.

对于多元拉普拉斯分布，我们推导出了多元拉普拉斯分布的二次型的方差，两个二次型的协方差，线性函数与二次型的协方差。

Unlike the previous method, however, the proposed algorithm obtains the new transformed matrix by taking the conjugate operation to the forward and backward spatial smoothing covariance matrix of the received data, so that the modified covariance differencing matrix which is a Hermitian matrix of zero real part can lower the computation load effectively during the eigenvalue decomposition.

不同于以前的算法，提出的算法通过对接收数据的前后向空间平滑协方差矩阵进行共轭运算得到新的变换矩阵，因此修正后的协方差差分矩阵是个实部为零的厄米特矩阵，从而有效地降低了特征分解过程的计算量。

In order to reduce calculation error, the frequency distribution of average values is used to compute the mixed distribution's digital features of each component distribution, thereinto, the number of the component distribution is determined by AIC, choose the number that meets the minimum value of AIC as the component number of mixed distribution, and the other parameters are estimated by EM algorithm; Secondly, because each component distribution is corresponding to a kind of major gene genotype, according to the values of the average and variance of the each component distribution, we can use the limit error of the normal distribution to plot each individual into the correspondent component distribution, namely into correspondent major gene genotype. Then we regard each major gene genotype as a treatment level of one-way analysis of variances, and the one-way multivariate analysis of variance is carried out to calculate the covariance matrix of major gene effect, covariance matrix of polygene effect, covariance matrix of environment effect and so on; At last, combining the weights of the each component distribution of mixed distribution, we can calculate the variance of major gene effect, the variance of polygene effect, environmental variance and the genetic gain of the quantitative trait.

为减小计算误差，本研究采用均值的频数分布来计算各成分分布的数字特征，其中成分分布个数根据AIC准则，选择使AIC值达到最小的成分分布个数作为混合分布的成分分布数，分布中其它参数的确定利用EM算法来估计；其次，每个成分分布对应一种主基因基因型，根据各个成分分布的均值和方差，利用正态分布的极限误差将每个个体划分到相应的成分分布中，即相应的主基因基因型中，将每种主基因型作为单因素方差分析的一个处理水平，对其进行单因素的多元方差分析，分别计算主基因效应协方差阵、多基因效应协方差阵、环境协方差阵等参数；最后结合混合分布中各成分分布的权重即各主基因基因型的分离比例，计算主基因效应方差，多基因效应方差和环境方差，以及遗传力等参数，进而计算该数量性状的遗传进展。

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。

The past works on coupling eddy covariance technique and remote sensing technology are summarized. The following three aspects were focused:①Cross validation on the carbon flux estimation between eddy covariance and remote sensing technology;②Eddy covariance provides ground parameters for remote sensing inversion;③Remote sensing based eddy footprint analysis.

总结过去耦合涡度协方差技术与遥感技术的工作，主要在以下3个层面展开：①涡度协方差技术与遥感技术对碳通量估算的相互验证；②涡度协方差技术为遥感反演提供地面参数；③遥感观测解译辅助分析通量贡献区。

Throughout comparing the above estimating methods, we have the following results: the estimators of the moments of the errors does not depend on the random effects, and that of the random effects does not depend on the errors, and then the corresponding asymptotic variances are very simple and optimal; when the random effects are multivariate, we can not construct different estimating equations for the random effects and errors respectively, which results that the asymptotic covariances of estimation are very complex and then the estimating efficiency is bad.

比较上述两种估计法，我们发现：当随机效应是一维的时侯，误差的各阶矩的估计不依赖不可观测的随机效应，随机效应的估计也不依赖误差，因此，估计的渐近方差结构特别简单也是最优的；而当随机效应是多维的，因为随机效应的协变量的影响，我们没有办法针对随机效应和误差的各阶矩分别建立估计方程，这导致所得的估计的渐近方差或者协方差矩阵特别复杂，从而估计的效果不是很好。

This demonstrates that it is necessary to carry out variance-covariance component estimation for the double-differenced GPS observables.

结果 两种方法估计的方差－协方差阵都与常用的先验协方差阵有明显区别；两种方法估计计算结果非常接近。

与协方差相关的网络解释 [注：此内容来源于网络，仅供参考]

autocovariance function：自协方差函数

autocovariance 自协方差 | autocovariance function 自协方差函数 | autodistributivity 自分配性

autocovariance spectrum：自协方差谱

"autocovariance","自协方差" | "autocovariance spectrum","自协方差谱" | "autoregressive integrated moving average (ARIMA)","自回归积分移动平均"

covariance function：协方差函数

协调世界时时号 time signal in UTC | 协方差函数 covariance function | 协方差函数 covariance function

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 functional：协方差泛函数

covariance function 协方差函数 | covariance functional 协方差泛函数 | covariance matrix 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：样本协方差阵

sample covariance 样本协方差 | sample covariance matrix 样本协方差阵 | sample dispersion 样本方差

sample covariance coefficient：样本协方差系数

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

covariance for stratified sampling：分层抽样协方差

covariance between inheritance and environment 遗传环境协方差 | covariance for stratified sampling 分层抽样协方差 | covariance function 积差函数,协方差函数=>共分散関数

within groups：类内协方差矩阵

Use Covariance Mathix 用协方差矩阵 | Within-groups 类内协方差矩阵 | Separate-groups 类协方差矩阵