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1.
Unscented transformation with scaled symmetric sampling strategy for precision estimation of total least squares 总被引：2，自引：0，他引：2
The errors-in-variables (EIV) model is a nonlinear model, the parameters of which can be solved by singular value decomposition (SVD) method or the general iterative algorithm. The existing formulae for covariance matrix of total least squares (TLS) parameter estimates don’t fully consider the randomness of quantities in iterative algorithm and the biases of parameter estimates and residuals. In order to reflect more reasonable precision information for TLS adjustment, the derivative-free unscented transformation with scaled symmetric sampling strategy, i.e. scaled unscented transformation (SUT), is introduced and implemented. In this contribution, we firstly discuss the existing various solutions of TLS adjustment and covariance matrices of TLS parameter estimates and derive the general first-order approximate cofactor matrices of random quantities in TLS adjustment. Secondly, based on the combination of TLS iterative algorithm and calculation process of SUT, we design the two SUT algorithms to calculate the biases and the second-order approximate covariance matrices. Finally, the straight line fitting model and plane coordinate transformation model are used to demonstrate that applying SUT for precision estimation of TLS adjustment is feasible and effective. 相似文献
2.
Data-snooping procedure applied to errors-in-variables models 总被引：1，自引：0，他引：1
The theory of Baarda’s data snooping — normal and F tests respectively based on the known and unknown posteriori variance — is applied to detect blunders in errors-invariables (EIV) models, in which gross errors are in the vector of observations and/or in the coefficient matrix. This work is a follow-up to an earlier work in which we presented the formulation of the weighted total least squares (WTLS) based on the standard least squares theory. This method allows one to directly apply the existing body of knowledge of the least squares theory to the errors-in-variables models. Among those applications, data snooping methods in an EIV model are of particular interest, which is the subject of discussion in the present contribution. This paper generalizes the Baarda’s data snooping procedure of the standard least squares theory to an EIV model. Two empirical examples, a linear regression model and a 2-D affine transformation, using simulated and real data are presented to show the efficacy of the presented formulation. It is highlighted that the method presented is capable of detecting outlying equations (rather than outlying observations) in a straightforward manner. Further, the WTLS method can be used to handle different TLS problems. For example, the WTLS problem for the conditions and mixed models, the WTLS problem subject to constraints and variance component estimation for an EIV model can easily be established. These issues are in progress for future publications. 相似文献
3.
Variance component estimation in errors-in-variables models and a rigorous total least-squares approach 总被引：1，自引：0，他引：1
Vahid Mahboub 《Studia Geophysica et Geodaetica》2014,58(1):17-40
A method for variance component estimation (VCE) in errors-in-variables (EIV) models is proposed, which leads to a novel rigorous total least-squares (TLS) approach. To achieve a realistic estimation of parameters, knowledge about the stochastic model, in addition to the functional model, is required. For an EIV model, the existing TLS techniques either do not consider the stochastic model at all or assume approximate models such as those with only one variance component. In contrast to such TLS techniques, the proposed method considers an unknown structure for the stochastic model in the adjustment of an EIV model. It simultaneously predicts the stochastic model and estimates the unknown parameters of the functional model. Moreover the method shows how an EIV model can support the Gauss-Helmert model in some cases. To make the VCE theory into EIV model more applicable, two simplified algorithms are also proposed. The proposed methods can be applied to linear regression and datum transformation. We apply these methods to these examples. In particular a 3-D non-linear close to identical similarity transformation is performed. Two simulation studies besides an experimental example give insight into the efficiency of the algorithms. 相似文献
4.
针对Mogi模型垂直位移与水平位移联合反演中的病态问题,改进火山形变总体最小二乘(Total Least Squares,TLS)联合反演的虚拟观测法,并使用方差分量估计(Variance Components Estimation,VCE)方法确定病态问题的正则化参数.将附有先验信息的参数作为观测方程,与垂直位移和水平位移的观测方程联合解算,推导了三类观测方程联合反演的求解公式及基于总体最小二乘方差分量估计确定正则化参数的表达式,给出了算法的迭代流程.通过算例实验,研究了总体最小二乘联合反演的虚拟观测法在火山Mogi模型形变反演中的应用;算例结果表明,三类数据的联合平差及方差分量估计方法可以确定权比因子并得到修正后的压力源参数,具有一定的实际参考价值. 相似文献
5.
利用最小二乘配置进行地壳形变分析,其结果的合理性关键在于经验协方差函数的拟合.考虑到观测数据存在粗差的情况,提出基于观测值中位数初值的抗差最小二乘配置方法和基于中位参数法的抗差最小二乘配置方法.两种方法首先分别利用观测值中位数给出观测值初始权阵以及利用中位参数法给出最小二乘配置初始解,然后均在给定协方差函数参数初始值的情况下,应用合适的等价权进行抗差估计并通过迭代计算,最终获得稳健的协方差函数参数估值及最小二乘配置解.利用本文提出的两种方法以及传统方法分别对庐山地震的GPS垂直位移数据和意大利L'Aquila地震的InSAR同震位移数据进行处理分析.结果表明:相对传统方法,基于观测值中位数初值的抗差最小二乘配置方法效果更好,更具稳健性. 相似文献
6.
An algorithmic approach to the total least-squares problem with linear and quadratic constraints 总被引：5，自引：0，他引：5
Proper incorporation of linear and quadratic constraints is critical in estimating parameters from a system of equations.
These constraints may be used to avoid a trivial solution, to mitigate biases, to guarantee the stability of the estimation,
to impose a certain “natural” structure on the system involved, and to incorporate prior knowledge about the system. The Total
Least-Squares (TLS) approach as applied to the Errors-In-Variables (EIV) model is the proper method to treat problems where
all the data are affected by random errors. A set of efficient algorithms has been developed previously to solve the TLS problem,
and a few procedures have been proposed to treat TLS problems with linear constraints and TLS problems with a quadratic constraint.
In this contribution, a new algorithm is presented to solve TLS problems with both linear and quadratic constraints. The new
algorithm is developed using the Euler-Lagrange theorem while following an optimization process that minimizes a target function.
Two numerical examples are employed to demonstrate the use of the new approach in a geodetic setting. 相似文献
7.
8.
When gravimetric data observations have outliers, using standard least squares (LS) estimation will likely give poor accuracies
and unreliable parameter estimates. One of the typical approaches to overcome this problem consists of using the robust estimation
techniques. In this paper, we modified the robust estimator of Gervini and Yohai (2002) called REWLSE (Robust and Efficient Weighted Least Squares Estimator), which combines simultaneously high statistical efficiency
and high breakdown point by replacing the weight function by a new weight function. This method allows reducing the outlier
impacts and makes more use of the information provided by the data. In order to adapt this technique to the relative gravity
data, weights are computed using the empirical distribution of the residuals obtained initially by the LTS (Least Trimmed
Squares) estimator and by minimizing the mean distances relatively to the LS-estimator without outliers. The robustness of
the initial estimator is maintained by adapted cut-off values as suggested by the REWLSE method which allows also a reasonable
statistical efficiency. Hereafter we give the advantage and the pertinence of REWLSE procedure on real and semi-simulated
gravity data by comparing it with conventional LS and other robust approaches like M- and MM-estimators. 相似文献
9.
Vahid Mahboub 《Studia Geophysica et Geodaetica》2016,60(2):195-209
A weighted least-squares (WLS) solution to a 3-D non-linear symmetrical similarity transformation within a Gauss-Helmert (GH) model, and/or an errors-in-variables (EIV) model is developed, which does not require linearization. The geodetic weight matrix is the inverse of the observation dispersion matrix (second-order moment). We suppose that the dispersion matrices are non-singular. This is in contrast to Procrustes algorithm within a Gauss-Markov (GM) model, or even its generalized algorithms within the GH and/or EIV models, which cannot accept geodetic weights. It is shown that the errors-invariables in the source system do not affect the estimation of the rotation matrix with arbitrary rotational angles and also the geodetic weights do not participate in the estimation of the rotation matrix. This results in a fundamental correction to the previous algorithm used for this problem since in that algorithm, the rotation matrix is calculated after the multiplication by row-wise weights. An empirical example and a simulation study give insight into the efficiency of the proposed procedure. 相似文献
10.
The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error‐contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors‐in‐variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
11.
12.
基于反射波动方程,本文提出了一种估计地下反射率分布的地震数据最小二乘偏移方法.高频近似下,非齐次的一次反射波动方程的源项是由反射率与入射波场的时间一阶导数相互作用产生的.根据反射波动方程,利用线性最小二乘反演方法由地震反射数据重建出地下产生反射波的反射源,再结合波场正演计算出的地下入射波场,得到地下反射率分布的估计.在地下反射源的线性最小二乘反演重建中,我们采用迭代求解方法,并以地震波的检波器单向地下照明强度作为最小二乘优化问题中Hessian矩阵的近似. 相似文献
13.
Studia Geophysica et Geodaetica - In this contribution, an iterative algorithm for variance-covariance component estimation based on the structured errors-in-variables (EIV) model is proposed. We... 相似文献
14.
Bayesian inference for the Errors-In-Variables model 总被引：1，自引：0，他引：1
Xing Fang Bofeng Li Hamza Alkhatib Wenxian Zeng Yibin Yao 《Studia Geophysica et Geodaetica》2017,61(1):35-52
We discuss the Bayesian inference based on the Errors-In-Variables (EIV) model. The proposed estimators are developed not only for the unknown parameters but also for the variance factor with or without prior information. The proposed Total Least-Squares (TLS) estimators of the unknown parameter are deemed as the quasi Least-Squares (LS) and quasi maximum a posterior (MAP) solution. In addition, the variance factor of the EIV model is proven to be always smaller than the variance factor of the traditional linear model. A numerical example demonstrates the performance of the proposed solutions. 相似文献
15.
微地震监测难以拾取准确初至,为了提高反演定位精度和减小多解性,研究了微地震贝叶斯差分进化反演方法.从分析讨论理论模型反演残差及其协方差分布特征出发,结合对比不加噪音和加入不同程度的噪音后残差协方差极小点位置移动、分布梯度变化特征,提出了先验信息解估计方法.针对后验估计中,由于难以获得先验信息解的方差估计致使无法计算加权系数问题,通过分析残差变化特征和解的变化关系,研究了利用残差求取加权系数的方法.为了加快寻优速度,讨论了差分进化反演方法,在变异操作方面使用差分策略,即利用种群中个体间的差分向量对个体进行扰动,实现个体变异,充分有效利用群体分布特性,提高算法的搜索能力,避免遗传算法中变异方式的不足.通过理论模型测试本方法的反演效果,并且和搜索方法反结果进行比较.测试结果证明本反演方法,对于不同程度初至干扰,反演结果向准确解逼近程度比搜索方法要好得多,实际资料的反演结果也好于搜索方法. 相似文献
16.
Yan Zunguo 《中国地震》1994,(4)
文中依据古登堡－里克特（Ｇ－Ｒ）震级－频度公式，结合实例说明了参数ｂ值的稳健估计方法，并将其与最小二乘法及最大似然法比较。结果表明，一般情况下，稳健估算结果与实际数据分布较吻合，并且可以在不剔除低震级段和大震级段的偏离分布值的情况下，取得与选取合理震级上下限后最小二乘法一致的结果。此外，稳健估计结果的余差分析同样也得到地震震级－频度关系不是简单的对数线性关系的结果，而原始Ｇ－Ｒ公式需加二次以上的高次项才能较好地描述该特性。 相似文献
17.
Application of periodic autoregressive process to the modeling of the Garonne river flows 总被引：1，自引：1，他引：0
Eugen Ursu Jean-Christophe Pereau 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(7):1785-1795
Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and to estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959–2010) for the Garonne river in the southwest of France. Results show that the accuracy of forecasts is improved in the robust model with respect to the unrobust model for the quarter-monthly flows. By reducing the number of parameters to be estimated, the principle of parsimony favors the choice of the robust approach. 相似文献
18.
A method for inverting electromagnetic fields induced by a line source in an earth of two-dimensional conductivity structure is developed. Certain unique features of the finite element method are used to construct an efficient algorithm for the accurate calculation of the Jacobian matrix of partial derivatives, and the resulting linearized equations are solved using the damped least squares method. Case studies of theoretical data generated from a simple model of interest in geophysical prospecting show that, in general, it is impossible to obtain, from surface data alone, accurate estimates of the conductivity of structures buried deeper than 0.2 skin depths under a conducting overburden. The addition of borehole data to the surface data is found to increase the resolving power of the electromagnetic method dramatically. In particular, the borehole data appear to stabilize the inverse when only a poor initial estimate of the likely structure is given. 相似文献
19.
采用现有的估计模型对混凝土建筑结构的抗毁性进行估计时,存在估计精度低、耗时长等问题。为此,提出一种基于最小二乘支持向量机的强震作用下混凝土高层建筑结构的抗毁性估计模型。该模型采用最小二乘支持向量机对混凝土结构强震损伤程度相关数据的训练样本进行训练,创建混凝土结构抗毁性估计模型;为了减少可能存在的模型误差,采用KLASSO调参模型对结构抗毁性估计模型中的参数进行调节和优化,得出可靠、稳定的强震作用下混凝土高层建筑结构抗毁性估计模型。仿真实验证明,该模型估计精度相对较高,可节省估计用时,为更好地提升建筑行业的安全检测工作效率提供很好的依据。 相似文献
20.
Iteratively re‐weighted and refined least squares algorithm for robust inversion of geophysical data 
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下载免费PDF全文 A robust metric of data misfit such as the ?1‐norm is required for geophysical parameter estimation when the data are contaminated by erratic noise. Recently, the iteratively re‐weighted and refined least‐squares algorithm was introduced for efficient solution of geophysical inverse problems in the presence of additive Gaussian noise in the data. We extend the algorithm in two practically important directions to make it applicable to data with non‐Gaussian noise and to make its regularisation parameter tuning more efficient and automatic. The regularisation parameter in iteratively reweighted and refined least‐squares algorithm varies with iteration, allowing the efficient solution of constrained problems. A technique is proposed based on the secant method for root finding to concentrate on finding a solution that satisfies the constraint, either fitting to a target misfit (if a bound on the noise is available) or having a target size (if a bound on the solution is available). This technique leads to an automatic update of the regularisation parameter at each and every iteration. We further propose a simple and efficient scheme that tunes the regularisation parameter without requiring target bounds. This is of great importance for the field data inversion where there is no information about the size of the noise and the solution. Numerical examples from non‐stationary seismic deconvolution and velocity‐stack inversion show that the proposed algorithm is efficient, stable, and robust and outperforms the conventional and state‐of‐the‐art methods. 相似文献