Comparative Study of Response Surface Designs with Errors-in-Variables Model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.

Error model identification of inertial navigation platform based on errors-in-variables model

Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform, both the input and output data contain noises. In this case, the conventional regression model and the least squares （LS） method will result in bias. Based on the models of inertial navigation platform error and observation error, the errors-in-variables （EV） model and the total least squares （TLS） method axe proposed to identify the error model of the inertial navigation platform. The estimation precision is improved and the result is better than the conventional regression model based LS method. The simulation results illustrate the effectiveness of the proposed method.

基于errors-in-variables的预测模型及其应用

预测是统计学实际应用的一个主要方面,多元线性回归预测是一种很好的方法,广泛地应用在各种实际领域,但其局限性及不足也是明显的。本文以一种新的观点认识数据,即认为变量的观测里均含有误差,同时认为不应删除经慎重选择进来的解释变量。为此,本文提出了一种新的多元预测方法———多元线性EIV预测。本文还考虑了新预测模型的一个实例应用,并从相对偏差上与多元回归预测进行了比较,从而揭示了多元线性EIV预测的先进性及较好的预测精度。

Errors-in-variables模型的参数估计

介绍了Errors_in_variables模型 ,利用Fisher得分算法 ,给出了在自变量的随机影响因素和因变量的随机影响因素相互独立和无重复测量数据情况下Errors_in_variables模型参数估计的迭代公式 .

APPLICATION OF FRF ESTIMATOR BASED ON ERRORS-IN-VARIABLES MODEL IN MULTI-INPUT MULTI-OUTPUT VIBRATION CONTROL SYSTEM

The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.

一类线性errors－in－variables模型的参数强相合估计及其a．s．收敛速度

本文讨论了如下一类线性ｅｒｒｏｒｓ－ｉｎ－ｖａｒｉａｂｌｅｓ模型——多元线性结构关系模型β′ｘｋ＋α＝０，ξｋ＝ｘｋ＋εｋ．｛ｋ＝１，２，…，ｎ．其中，｛ｘｋ：ｋ＝１，２，…，ｎ｝为一组ｉ．ｉ．ｄ．的ｍ维随机向量，｛εｋ：ｋ＝１，２，…，ｎ｝是ｉ．ｉ．ｄ．的随机误差，Ｅ（ε１）＝０，Ｖａｒ（ε１）＝σ２Ｉｍ．且｛ｘｋ：ｋ＝１，２，…，ｎ｝与｛εｋ：ｋ＝１，２，…，ｎ｝相互独立．在一些条件下，我们证明了估计量β，α，σ２的强相合性、唯一性，并给出了估计量的收敛速度为ｏ（ｎ－１－１ｑ），这里ｑ∈［１，２）．

Penalized total least squares method for dealing with systematic errors in partial EIV model and its precision estimation

When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.

一种基于PEIV模型的GNSS高程拟合解法

采用二次曲面函数拟合全球卫星导航系统(GNSS)高程时,系数矩阵中含有观测坐标,所以系数矩阵同样存在误差,故采用总体最小二乘拟合GNSS高程更加合理。但系数矩阵不同位置有相同的元素,为保证这些相同元素有相同的改正数,本文提出了基于部分变量含误差(PEIV)模型的GNSS高程拟合解法,该方法可以保证相同元素的改正数一致。最后通过模拟算例和实例算例分析发现,本文解法与加权总体最小二乘(WTLS)解算结果一致,验证了本文解法的可行性。

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

含缺失数据的EIV系统辨识

针对含缺失数据的变量带误差(EIV)系统,直接利用协方差匹配(CM)算法进行辨识的精度有限,为此提出一种协方差匹配迭代(covariance matching based iterative,CMI)算法。首先基于不完整数据集,利用CM算法获得模型参数的初始估计,然后采用交互估计理论,利用获得的参数计算缺失输出数据的估计,重构得到完整的数据集后再进一步利用CM算法更新参数估计。两者执行了递阶计算过程,通过迭代辨识逐步提高参数估计精度。仿真结果表明,CMI算法的参数估计误差在输出数据缺失率达到60%时仍然能够保持在2%以下,且随输入端和输出端噪信比的变化速率仅为CM算法的16.8%和10.8%,验证了所提算法具有较高的辨识精度和良好的鲁棒性。

基于第十三代国际地磁参考场模型在中国区域特征分析与研究

根据最新的第十三代国际地磁参考场模型(IGRF13),计算了2015—2020年中国区域地磁场模型七要素长期变化速率,并在此基础上分析我国区域地磁场长期变化特征。通过分析计算我国28个地磁台的IGRF13模型值与实际地磁场的长期变化速率、差值及均方误差,结果显示:IGRF13模型所显示的地磁场长期变化与我国区域地磁场实际观测变化基本一致,但在局部区域也存在差异,IGRF13模型能够体现中国区域地磁场的特征。应用IGRF13模型数据时需要考虑局部区域与台站实际观测数据的误差。

Efficient Statistical Inference for Partially Nonlinear Errors-in-Variables Models

In this paper, we consider the partially nonlinear errors-in-variables models when the non- parametric component is measured with additive error. The profile nonlinear least squares estimator of unknown parameter and the estimator of nonparametric component are constructed, and their asymptotic properties are derived under general assumptions. Finite sample performances of the proposed statistical inference procedures are illustrated by Monte Carlo simulation studies.

L-two-optimal identification of errors-in-variables models:a frequency-domain approach

This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIVM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate models are derived from the framework of an EIVM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities （LMIs）. In terms of the optimized system model, the noise model （NM） can be readily obtained by right multiplication of an inner. Compared with other EIVM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiabiUty. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.

Asymptotic Properties of Wavelet Estimators in Partially Linear Errors-in-variables Models with Long-memory Errors

While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables（EV） model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.

线性EIV模型的TLS估计及其典型应用

推导了将线性GM模型转换为变量含误差(EIV)模型并采用总体最小二乘(TLS)平差的方法,介绍了加权总体最小二乘、混合总体最小二乘和附限制条件的总体最小二乘问题及其解算方法。以经典大地测量控制网平差和数据拟合算例比较各种TLS估计的精度和计算效益。理论分析和算例表明:对于EIV模型,WTLS为最优估计,实际应用时需根据具体函数模型和随机假设选择合理的TLS平差方法。

Partial EIV模型的解法

提出了一种求解partial errors-in-variables(partial EIV)模型的思路。通过对partial EIV模型的部分元素进行移项,重组成新形式下的平差函数模型,两次运用间接平差原理分别求解平差参数与系数矩阵中的随机元素,把总体最小二乘平差问题转化为最小二乘平差问题,并通过适当变换提高了新解法的收敛速度。最后分别采用实测数据和模拟数据进行验证,求解了本文算法与已有算法的估值结果。算例结果表明,本文算法能取得与已有算法相同的结果,是切实可行的。

Some Properties of A Lack-of-Fit Test for a Linear Errors in Variables Model

The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary and sufficient condition for the equivalence. Therefore, testing for lack-of-fit in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption. A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis. Furthermore, we discuss the power of the test and the choice of the weight function involved in the test statistic.

Estimation of Parameters of Partially Linear Errors-in-variables Models with Replicated Net Points of Observation

A kind of partially linear errors-in-variables models with replicated net points of observation are studied in this paper. Estimators of unknown parameters are given. Under certain regular conditions, it is shown that the estimators of the unknown parameters are strongly consistent and their a.s. convergence rates are achieved.

线性Errors-in-Variables模型在确定汶川地震主震断层面中的应用

首先介绍线性Errors-in-Variables模型,给出求解回归系数的奇异值分解(SVD)算法和MATLAB源代码,其次指出在模型中所有变量均具有不可忽略的误差时,全最小二乘法得到回归系数估计更接近于模型中的真实系数,并通过理论分析和计算机仿真说明了这一结果,最后将线性模型和算法用于确定汶川大地震主震断层面,取得了与震源机制解一致的结果,说明了模型和算法的有效性。

Testing Lack-of-fit for a Polynomial Errors-in-variables Model

When a regression model is applied as an approximation of underlying model of data, the model checking is important and relevant. In this paper, we investigate the lack-of-fit test for a polynomial errorin-variables model. As the ordinary residuals are biased when there exist measurement errors in covariables, we correct them and then construct a residual-based test of score type. The constructed test is asymptotically chi-squared under null hypotheses. Simulation study shows that the test can maintain the signi.cance level well. The choice of weight functions involved in the test statistic and the related power study are also investigated. The application to two examples is illustrated. The approach can be readily extended to handle more general models.