非线性模型中方差和协方差分量的估计

采用差分代替微分的方法,并将非线性模型的似然函数分解为函数模型生成的似然函数和正交补似然函数(也是边缘似然函数)的乘积,由正交补似然函数得到非线性模型中严格的和简化的方差和协方差分量估计的迭代公式.很多学者提出的线性模型中方差和协方差分量估计的迭代公式都是本文的特殊情况.     

基于方差分量估计的拟合推估及其在GIS误差纠正的应用

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。     

作为质量指标的估计方差的可靠性

研究了具体有ｐ范分布的参数的极大似然估计的估计方差。除Ｌ２估计外,其余Ｌp估计的 估计方差会出现３种不同合理情况：零单位权方差和同一估计量有两个不同的估计方差。这说明极大似然估计与最小方差估计并不完全等价。产生的原因在于单位权方差与分布的总体方差不相等。定义了单位权估计方差与总体方差的比值作为估计方差的可靠性指标。ｐ＝２,估计方差的可靠性为１００％;ｐ＝∞时,可靠性为零;ｐ与２相关越大,估计方差的可     

采用方差-协方差分量估计GPS时间序列噪声特性

本文采用方差-协方差分量估计分析GPS残差时间序列噪声特性。介绍了该方法如何运用于GPS时间序列分析,详细的推导了函数模型,建立了数据处理流程。对比传统的极大似然估计,该方法可以定量计算各噪声分量的大小,并且具有计算速度快,数学模型严谨等优点。     

方差分量估计的通用公式

应用最小二乘原理将方差分量估计公式从参数平差模型推广到概括函数平差模型。通过选取恰当的权阵,基于概括函数模型的最小范数二次无偏估计及赫尔默特法得到的公式均是本文的特例。视协方差矩阵为权逆阵,得到了最小方差估计,并证明了该公式与最优二次无偏估计的通用公式等价,从而表明最优二次无偏估计和极大似然估计的通用公式也是本文的特例。除此之外,本文还给出了最小二乘方差分量估计的简化公式,并对其进行了扩展。最小二乘方差分量估计的假设检验理论同样得到了推广。     

作为质量指标的估计方差的可靠性

研究了具有p范分布的参数的极大似然估计的估计方差。除L     

方差—协方差分量极大似然估计的通用公式

本文由概括平差函数模型出发，按极大似然做估计原则导出了适用于所有平差函数模型的方差分量估计的通用公式，由Ｋ．Ｋｕｂｉｋ和Ｃ．Ｒ．Ｋｏｃｈ所导出的两个公式都是它的特例。     

方差的边缘极大似然估计

测量平差问题中,方差估计理论是复杂的。本文基于概括模型,组成自由项f(极大似然估计 MLE)的密度函数和改正数向量 V的线性函数(边缘极大似然估计 MMLE)的密度函数,详细推导了函数模型与随机模型中,未知参数 X与σ_0~2 的似然估计公式,分析了基于两种密度函数所得σ_0~2的似然估计存在差异的真正原因,并对两种方法所得的σ_0~2和X 的统计性质进行了讨论。指出边缘极大似然估计,σ_0~2 的具有良好的统计性质,可改善极大似然估计σ_0~2 的不定性(有偏);并且对任一平差模型的边缘极大似然估计,σ_0~2 无偏、有效的统计性质是一致的。     

附有约束最小二乘配置的信号与观测误差的方差分量估计

介绍方差-协方差分量估计理论的研究和发展情况,讨论最小二乘配置模型信号与观测误差的方差分量估计问题。在实际应用中,考虑到未知参数间存在几何或物理约束,针对附有约束条件最小二乘配置的方差分量估计的问题,基于Helmert方差分量估计原理,导出相应的计算公式。模拟算例结果表明,利用约束条件能够改善方差分量的估计精度,验证方法的有效性。     

库比克、威尔施对於宗俦教授的“方差——协方差分量估计的统一理论”等论文的评论——介绍与浅议

国际著名大地测量和摄影测量学家、澳大利亚昆士兰大学空间卫星导航中心工程博士K·库比克教授和国际著名大地和工程测量学家、联邦德国慕尼黑国防军大学工程博士W·威尔施教授,最近对武汉测绘科技大学教授、博士导师於宗俦所撰写的两篇论文《方差——协方差分量估计的统一理论》和《方差——协方差分量极大似然估计的通用公式》作了高度评价,现将他们的全文翻译于下。库比教授是这样评论的: 我想对於宗俦教授所写的论文《方差——协方差分量估计的统一理论》、《方差——协方差分量极大似然估计的通用公式》作一些评论。 方差分量的各种各样估计方法,如极大似然估计(ML)、最优二次无偏估计(BQUE)以及最小范数二次无偏估计(MINQUE)等等,是统计学家哈列(Hartley)和劳(Rao)所开创的(1967,1973)。从这以后,在这一领域内进行了大量的研究。然而,总的说来,统计学家是致力于误差向量的结构以及以一般模型为基础的估计量特性的研究上。而测量学家们则把他们的注意力局限于函数模型的结构上,这些模型是随着应用上的不同而变化很大。     

Adaptive collocation with application in height system transformation

In collocation applications, the prior covariance matrices or weight matrices between the signals and the observations should be consistent to their uncertainties; otherwise, the solution of collocation will be distorted. To balance the covariance matrices of the signals and the observations, a new adaptive collocation estimator is thus derived in which the corresponding adaptive factor is constructed by the ratio of the variance components of the signals and the observations. A maximum likelihood estimator of the variance components is thus derived based on the collocation functional model and stochastic model. A simplified Helmert type estimator of the variance components for the collocation is also introduced and compared to the derived maximum likelihood type estimator. Reasonable and consistent covariance matrices of the signals and the observations are arrived through the adjustment of the adaptive factor. The new adaptive collocation with related adaptive factor constructed by the derived variance components is applied in a transformation between the geodetic height derived by GPS and orthometric height. It is shown that the adaptive collocation is not only simple in calculation but also effective in balancing the contribution of observations and the signals in the collocation model.     

Maximum likelihood estimate of variance components

Summary Using the orthogonal complement likehood function, an iterative procedure for the maximum likelihood estimates of the variance and covariance components is derived. It is shown that these estimates are identical with the reproducing estimates of the locally best invariant quadratic unbiased estimation of variance and covariance components. Successive approximations of the maximum likelihood estimates are given in addition.     

Variance Component Estimation in Linear Inverse Ill-posed Models

Regularization has been applied by implicitly assuming that the weight matrix of measurements is known. If measurements are assumed to be heteroscedastic with different unknown variance components, all regularization techniques may not be proper to apply, unless techniques of variance component estimation are directly implemented. Although variance component estimation techniques have been proposed to simultaneously estimate the variance components and provide a means of regularization, the regularization parameter is treated as if it were also an extra variance component. In this paper, we assume no prior information on the model parameters and do not treat the regularization parameter as an extra variance component. Instead, we first analyze the biases of estimated variance components due to the regularization parameter and then propose bias-corrected variance component estimators. The results have shown that they work very well. Finally, we propose and investigate through simulations an iterative scheme to simultaneously estimate the variance components and the regularization parameter, in order to eliminate the effect of regularization parameter on variance components and the effect of incorrect prior weights or initial variance components on the regularization parameter.     

方差分量估计前提初探

根据方差分量估计理论,即使随机模型本身已经正确,方差分量估计也会得到不同于通常意义上的最优线性无偏最小二乘估计。此外,由于方差分量估计计算工作量一般较大,因此,本文提出了利用统计检验方法来判断是否进行方差分量估计的想法,并进行了初步研究。     

Estimability analysis of variance and covariance components

Although variance and covariance components have been extensively investigated and a number of elegant formulae to compute them have been derived, nothing is known, without any ambiguity, about their estimability in the case of a fully unknown variance–covariance matrix. We prove that variance and covariance components in this case are not estimable, thus clarifying the ambiguity of the literature on the topic and correcting some erroneous statements in the literature. We also give a new theorem on the estimability of a linear function of variance and covariance components. Then we propose a new method to estimate the variance–covariance matrix with special structure, which can presumably be represented by, at most, r(r + 1)/2 independent parameters to guarantee its estimability in such a subspace, by directly implementing the positive definiteness of the matrix as constraint to the restricted maximum likelihood method, where r is the number of redundant measurements. Therefore, our estimates of the variance and covariance components always reconstruct a positive definite matrix and are always physically meaningful.     

方差分量估计迭代算法收敛性分析与改进

本文首先对方差分量估计中的收敛性问题进行分析,指出方差分量的收敛性和估计方程的摄动性有关。提出了改进收敛性的思想,在此基础上提出了一种改进收敛的方法,用算例证明了所提出方法的有效性。