In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate ...
The paper presents a stochastic optimization algorithm for computing of least median of squares regr...
The first part of this paper gives a general approach to the least squares estimation of the weighti...
The theory of multivariate regression has been extensively studied and is commonly used in many dive...
Based on the Newton-Gauss iterative algorithm of weighted total least squares (WTLS), a robust WTLS ...
We review the development and extensions of the classical total least squares method and describe al...
We review the development and extensions of the classical total least squares method and describe al...
An estimate of a state vector for a physical system when the weight matrix in the method of least sq...
Thesis sumarizes basic theory required for inference of aproximation using the least squares method ...
Parameter estimation problems of mathematical models can often be formulated as nonlinear least squa...
In contrast to general optimization problems or optimal control problems it is not sufficient to cal...
A Newton method to solve total least squares problems for Toeplitz systems of equations is considere...
Parameter estimation problems of mathematical models can often be formulated as nonlinear least squa...
We consider a class of non-linear least squares problems that are widely used in fitting experimenta...
金沢大学理工研究域 電子情報学系In this letter, we introduce a predictor based least square (PLS) algorithm. By invo...
Abstract. Modeling the mean of a random variable as a function of unknown parameters leads to a nonl...
The paper presents a stochastic optimization algorithm for computing of least median of squares regr...
The first part of this paper gives a general approach to the least squares estimation of the weighti...
The theory of multivariate regression has been extensively studied and is commonly used in many dive...
Based on the Newton-Gauss iterative algorithm of weighted total least squares (WTLS), a robust WTLS ...
We review the development and extensions of the classical total least squares method and describe al...
We review the development and extensions of the classical total least squares method and describe al...
An estimate of a state vector for a physical system when the weight matrix in the method of least sq...
Thesis sumarizes basic theory required for inference of aproximation using the least squares method ...
Parameter estimation problems of mathematical models can often be formulated as nonlinear least squa...
In contrast to general optimization problems or optimal control problems it is not sufficient to cal...
A Newton method to solve total least squares problems for Toeplitz systems of equations is considere...
Parameter estimation problems of mathematical models can often be formulated as nonlinear least squa...
We consider a class of non-linear least squares problems that are widely used in fitting experimenta...
金沢大学理工研究域 電子情報学系In this letter, we introduce a predictor based least square (PLS) algorithm. By invo...
Abstract. Modeling the mean of a random variable as a function of unknown parameters leads to a nonl...
The paper presents a stochastic optimization algorithm for computing of least median of squares regr...
The first part of this paper gives a general approach to the least squares estimation of the weighti...
The theory of multivariate regression has been extensively studied and is commonly used in many dive...