自协方差

v，b的自协方差估计b均为渐近无偏估计；

For example , moment estimation (?) , of c_v and autocovariance estimation (?) of b are asymptotically unbiased estimators ;

双重时序模型矩估计的渐近性：1.样本自协方差（自相关）函数

Asymptotic properties of moment estimation for a doubly stochastic model : 1 . sample autocovariance ( autocorrelation ) function

多维ARMA（p，q）模型自协方差矩阵的一种算法

An Algorithm of Autocovariance Matrix for Multivariate ARMA ( p , q ) Models

表明：基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。

It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy .

但对于一般形式，目前，只知道其二阶结构（自协方差和谱）与线性ARMA模型相似。

According to the second order structure ( i. e. , auto - variance and spectral density ), it is similar to an ARMA model .

本文在平稳时序的离散模型基础上，利用自协方差函数、自谱函数、Green函数及频率特性，将离散模型转换为连续模型，并研究了这些转换之间的关系。

Based on a discrete model for stationary time series , the auto-covariance function , auto-spectrum function , Green 's function and frequency characteristics are used to transform the discrete model of time series into a continuous one . The relationships between these transformations are investigated .

针对MAR模型导出其自协方差函数的递推关系式及计算谱密度的算法，从而从理论上解决了这一模型的谱分析问题。

This paper derived the recursive formula of autocovariance function and a three-stage algorithm of computing spectral density of MAR model , thus we solved spectral analysis problem of MAR model .

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。

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 .

依据平稳随机过程&高斯过程的相关性质，利用其自协方差函数和TEC时间系列，构建了独立同标准正态分布的观测样本，并利用x~2假设检验的方法来探测电离层异常现象。

Based on this fact , here we made use of the time series of TEC and the auto-covariance function of the stationary process to construct independent identical distribution Gauss sample so that the X ~ 2 test can be used to detect the abnormity hidden in the sequence .

本文给出按0-1分布规律丢失值的平稳序列自协方差函数估计的渐近协方差，其结界主要是公式（9）及（10），类似于通常的Bartlett公式。

In this paper , we give estimated asymptotic covariance of autocovariance function of stationary series with missed value of 0 & 1 distribution . The results are formulas ( 9 ) and ( 10 ), which are similar to general bartlett 's formula .

回归分析中自协方差估计的渐近正态性

The asymptotic normality of the estimation of the autocovariance in regressive analysis

导出了系统各点随机信号的自协方差函数、功率谱密度函数；

Auto-covariances and power spectral density of the stochastic signals at every point in the systems ;

本文发现伽玛分布的尺度参数等于随机变量及其对数的协方差，并利用这一有趣性质构造伽玛分布参数的自协方差估计。

In this paper , we discover that the scale parameter of a gamma distribution is exactly equal to the covariance of the r. v. and its logarithm .

本文的目的在于，对于线性平稳时间序列的样本、自协方差、自相关和偏相关函数的渐近性质，给出一个比较系统的描述。

The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance , autocorrelation and partial autocorrelation functions of linear stationary time series .

因为主成分分析是建立在自协方差函数分解的基础上的，这一分解只能反映协变量和响应变量自身的信息，而没有考虑响应变量和自变量的相依关系。

Principal component analysis can only reflect the own information of the covariates and response variables and ignore their dependencies because it base on the decomposition of their auto-covariance function .

该算法使用的是接收信号的统计数据，根据接收信号自协方差矩阵最大特征值的特征向量去近似等于目标信号的控制向量。

This algorithm uses the statistics of the received signal , the eigenvector corresponding to the maximum eigen value of the auto covariance matrix of the received signal is approximately equal to the steering vector of the target signal source .

文中提出了两种处理空间自相关的方法：把自相关加入残差协方差作参数的改进估计相邻相关方法和把残差作区域化最优自相关估计一般空间自相关方法。

Two different ways of modeling spatially correlated errors are considered , of which the neighborhood-correlation method is used to modify the parameters estimation , and the spatial autocorrelation estimation of residuals is proposed to directly estimate the residuals .