加权总体最小二乘问题的分析

总体最小二乘问题由Golub和Van Loan首先进行数学的分析,随后人们对于总体最小二乘问题的算法、解的各种形式、总体最小二乘解和最小二乘解的关系、总体最小二乘解的扰动理论以及数值试验作了大量的研究工作。近来,[10]中给出了总体最小二乘问题(TLS)较一般地讨论。另一方面,Golub和Van Loan研究了总体最小二乘问题的特殊均加权形式。本文试图在[10,11]的基础上讨论最一般的总体最小二     

TLS问题和LS问题解加权残量的比较

总体最小二乘(TLS)问题和最小二乘(LS)问题解残量的比较已有多篇文献予以探讨.本文对TLS问题和LS问题解的加权残量进行了比较. 导出了TLS解、改进的LS解及普通LS解加权残量之间的误差界. 从而进一步完善了已有的相关结果.     

线性流形上矩阵方程B^TXB=D的加权最小二乘对称解和解的最佳逼近

本文利用矩阵的奇异值分解（SVD）,给出了在一流形上矩阵方程B^TXB=D的加权最小二乘对称解的通解表达式,并解决了加权最小二乘对称解的最佳逼近问题。     

关于用加权最小二乘法求切比雪夫解的研究

本文给出了加权最小二乘解的交错定理,同时又证明了存在一加权最小二乘解就是切比雪夫解(逼近空间是代数多项式类|P~n),并对|P~n提供了有限次求得切比雪夫解的权因子选取方法。     

基于最小二乘加权支持向量机的个人信用预测模型研究

针对不同类别样本数差异和不同误分代价的分类问题，提出了一种基于最小二乘加权支持向量机的分类预测方法。在最小二乘加权支持向量机的基础上，考虑不同类别样本数差异和不同误分代价，提出了新的最小二乘加权支持向量机分类模型，构造了新的最优分类函数。将该模型应用于个人信用预测实验，与已有方法的对比实验结果表明，提出的模型在解决不同类别样本数差异和不同误分代价的个人信用预测问题时，有效地降低了总误分代价，提高了个人信用预测精确度。     

最小二乘问题及其研究

本文从理论上讨论了线性方程中最小二乘解的存在性及最小范数最小二乘解的唯一性，并给出求最小二乘解及最小范数最小二乘解的公式方法。     

Hermitian自反矩阵反问题的最小二乘解

本文研究了Hermitian自反矩阵反问题的最小二乘解及其最佳逼近.利用矩阵的奇异值分解理论,获得了最小二乘解的表达式.同时对于最小二乘解的解集合,得到了最佳逼近解.     

线性流形上D对称矩阵反问题的最小二乘解

本研究了线性流形上D对称矩阵反问的最小二乘解及其逼近问题，给出了最小二乘解的一般表达式，并就该问题的特殊情况－矩阵反问题，获得了有解的充分必要条件，并在有解的条件下得到了解的一段表达式。     

大规模加权总体最小二乘问题的迭代算法

变量含误差(EIV)模型常常用加权总体最小二乘方法估计参数,但是当系数矩阵为大规模稀疏阵时,该算法会花费较大的计算量和存储空间.为了控制存储和计算量,提出了一种基于加权Rayleigh商的迭代算法.数值算例表明,与经典的总体最小二乘算法相比,新算法减少了计算量和存储空间,并且能更好地估计参数.     

等式约束不定最小二乘问题的双曲MGS消去算法

众所周知,加权法是解等式约束不定最小二乘问题的方法之一.通过探讨极限意义下,双曲MGS算法解对应加权问题的本质,得到一类消去算法.实验表明,该算法以和文献中现有的GHQR算法达到一样的精度,但实际计算量只需要GHQR算法的一半.     

Algebraic relations between the total least squares and least squares problems with more than one solution

Summary This paper completes our previous discussion on the total least squares (TLS) and the least squares (LS) problems for the linear systemAX=B which may contain more than one solution [12, 13], generalizes the work of Golub and Van Loan [1,2], Van Huffel [8], Van Huffel and Vandewalle [11]. The TLS problem is extended to the more general case. The sets of the solutions and the squared residuals for the TLS and LS problems are compared. The concept of the weighted squares residuals is extended and the difference between the TLS and the LS approaches is derived. The connection between the approximate subspaces and the perturbation theories are studied.It is proved that under moderate conditions, all the corresponding quantities for the solution sets of the TLS and the modified LS problems are close to each other, while the quantities for the solution set of the LS problem are close to the corresponding ones of a subset of that of the TLS problem.This work was financially supported by the Education Committee, People's Republic of China     

Detection of outliers in weighted least squares regression

In multiple linear regression model, we have presupposed assumptions (independence, normality, variance homogeneity and so on) on error term. When case weights are given because of variance heterogeneity, we can estimate efficiently regression parameter using weighted least squares estimator. Unfortunately, this estimator is sensitive to outliers like ordinary least squares estimator. Thus, in this paper, we proposed some statistics for detection of outliers in weighted least squares regression.     

Tikhonov regularization for weighted total least squares problems

In this work, we study and analyze the regularized weighted total least squares (RWTLS) formulation. Our regularization of the weighted total least squares problem is based on the Tikhonov regularization. Numerical examples are presented to demonstrate the effectiveness of the RWTLS method.     

ON STABLE PERTURBATIONS OF THE STIFFLY WEIGHTED PSEUDOINVERSE AND WEIGHTED LEAST SQUARES PROBLEM

In this paper we study perturbations of the stiffly weighted pseudoinverse （W^1/2 A）^＋W^1/2 and the related stiffly weighted least squares problem, where both the matrices A and W are given with W positive diagonal and severely stiff. We show that the perturbations to the stiffly weighted pseudoinverse and the related stiffly weighted least squares problem are stable, if and only if the perturbed matrices A = A ＋ δA satisfy several row rank preserving conditions.     

A weighted least squares method for scattered data fitting

In this paper, we present a weighted least squares method to fit scattered data with noise. Existence and uniqueness of a solution are proved and an error bound is derived. The numerical experiments illustrate that our weighted least squares method has better performance than the traditional least squares method in case of noisy data.     

Iterative weighted partial spline least squares estimation in semiparametric modeling of longitudinal data

In this paper we consider the estimating problem of a semiparametric regression modelling whenthe data are longitudinal.An iterative weighted partial spline least squares estimator(IWPSLSE)for the para-metric component is proposed which is more efficient than the weighted partial spline least squares estimator(WPSLSE)with weights constructed by using the within-group partial spline least squares residuals in the sense     

Error estimation for a weighted minimum-norm least squares solution with positive definite weights

The weighted least squares problem is considered. Given a generally inconsistent system of linear algebraic equations, error estimates are obtained for its weighted minimum-norm least squares solution under perturbations of the matrix and the right-hand side, including the case of rank modifications of the perturbed matrix.     

Latent scale linear models for multivariate ordinal responses and analysis by the method of weighted least squares

Summary A multivariate latent scale linear model is defined for multivariate ordered categorical responses and inference procedures based on the weighted least squares method are developed. Several applications of the model are suggested and illustrated through an analysis of real data. Asymptotic properties of the weighted least squares method are examined and some consequences of misspecification of the model are also discussed.     

Regularized total least squares based on the nonlinear Arnoldi method

A computational approach for solving regularized total least squares problems via a sequence of quadratic eigenvalue problems has recently been introduced by Sima, Van Huffel, and Golub. Combining this approach with the nonlinear Arnoldi method and reusing information from all previous quadratic eigenvalue problems, together with an early update of search spaces we arrive at a very efficient method for large regularized total least squares problems. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)