共查询到10条相似文献，搜索用时 140 毫秒
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针对Partial EIV模型的方差分量估计中未考虑参数估值偏差所带来的影响,将Partial EIV模型视为非线性函数得到参数估值的偏差及二阶近似协方差表达式,计算得到偏差改正后的参数估值,结合方差分量估计方法,更新由参数估值影响的矩阵变量,给出了基于偏差改正的方差分量估计迭代方法。试验结果表明,参数估值及其协方差主要受参数估值偏差大小的影响,加入偏差改正能够得到更加合理的参数估值及方差分量估值,偏差改正后的方差分量估值可更加合理地评估参数估值的精度信息。 相似文献
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Khosro Moghtased-Azar Ramin Tehranchi Ali Reza Amiri-Simkooei 《Journal of Geodesy》2014,88(5):427-439
A typical problem of estimation principles of variance and covariance components is that they do not produce positive variances in general. This caveat is due, in particular, to a variety of reasons: (1) a badly chosen set of initial variance components, namely initial value problem (IVP), (2) low redundancy in functional model, (3) an improper stochastic model, and (4) data’s possibility of containing outliers. Accordingly, a lot of effort has been made in order to design non-negative estimates of variance components. However, the desires on non-negative and unbiased estimation can seldom be met simultaneously. Likewise, in order to search for a practical non-negative estimator, one has to give up the condition on unbiasedness, which implies that the estimator will be biased. On the other hand, unlike the variance components, the covariance components can be negative, so the methods for obtaining non-negative estimates of variance components are not applicable. This study presents an alternative method to non-negative estimation of variance components such that non-negativity of the variance components is automatically supported. The idea is based upon the use of the functions whose range is the set of all positive real numbers, namely positive-valued functions (PVFs), for unknown variance components in stochastic model instead of using variance components themselves. Using the PVF could eliminate the effect of IVP on the estimation process. This concept is reparameterized on the restricted maximum likelihood with no effect on the unbiasedness of the scheme. The numerical results show the successful estimation of non-negativity estimation of variance components (as positive values) as well as covariance components (as negative or positive values). 相似文献
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Least-squares variance component estimation 总被引：19，自引：15，他引：4
Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown
variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible
because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing
body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its
various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix
from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We
also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE.
We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of
VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of
nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected
by the results of LS-VCE. Various examples are given to illustrate the theory. 相似文献
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基于方差分量估计的拟合推估及其在GIS误差纠正的应用 总被引：2，自引：0，他引：2
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。显然观测噪声的先验方差协方差阵与拟合得到的随机信号的方差协方差矩阵必须相互协调,即观测噪声向量和信号向量的权矩阵所对应的方差因子应该一致,否则将对固定效应和随机效应参数的估计带来系统性的影响。应用方差分量估计来协调拟合推估模型中观测噪声和信号向量的随机模型,并分别从极大似然估计、MINQUE估计、赫尔默特方差分量估计三方面构建了拟合推估模型的方差分量解,最后利用新提出的理论与方法,对一幅实际的扫描地形图进行误差纠正,结果表明基于方差分量估计的拟合推估法能够提高扫描地形图的精度。 相似文献
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本文探讨了整体大地测量平差中的方差分量估计问题,给出了自适应最小二乘配置;不仅较好地解决了各类观测量权比的确定问题,而且能够确定信号的方差因子。此外,文章还对如何更好地确定局部地区扰动位及其泛函的协方差函数作了讨论;最后通过对一个实测网进行了整体平差,并与经典平差结果进行了比较。 相似文献
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Application of least squares variance component estimation to errors-in-variables models 总被引：3，自引：2，他引：1
A. R. Amiri-Simkooei 《Journal of Geodesy》2013,87(10-12):935-944
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随机变量经非线性变换后统计性质的确定 总被引：1，自引：0，他引：1
扩展卡尔曼滤波应用于非线性系统的状态估计时，首先要对系统的动力方程进行线性化，这样就给状态的估计带来一定的误差。文中通过对非线性变换的函数进行级数展开，获得了非线性变换后随机变量的真实均值和协方差的表达式，并得到一阶线性化的形式；然后提出了一种精度更高的变换算法，并从理论上证明了该变换算法较之线性化的做法能够更好地逼近非线性变换后的真实均值和协方差，数值试验也证明了这一点。 相似文献