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On weighted total least-squares adjustment for linear regression
Journal of Geodesy, 2007The weighted total least-squares solution (WTLSS) is presented for an errors-in-variables model with fairly general variance–covariance matrices. In particular, the observations can be heteroscedastic and correlated, but the variance–covariance matrix of the dependent variables needs to have a certain block structure.
Burkhard Schaffrin, Andreas Wieser
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Web image interpolation via weighted total least squares regression
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Although ordinary least squares (OLS) regression achieves great success in clean image interpolation, its effectiveness is questionable in the scenario of web images which are usually compressed beforehand. The inherent flaw of OLS is that it is asymmetric, the perturbation is only confined on the right side of the linear system.
Xianming Liu +4 more
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2020
<p>The errors-in-variables (EIV) model is applied to surveying and mapping fields such as empirical coordinate transformation, line/plane fitting and rigorous modelling of point clouds and so on as it takes the errors both in coefficient matrix and observation vector into account.
Xie Jian, Long Sichun
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<p>The errors-in-variables (EIV) model is applied to surveying and mapping fields such as empirical coordinate transformation, line/plane fitting and rigorous modelling of point clouds and so on as it takes the errors both in coefficient matrix and observation vector into account.
Xie Jian, Long Sichun
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An iterative solution of weighted total least-squares adjustment
Journal of Geodesy, 2010Total least-squares (TLS) adjustment is used to estimate the parameters in the errors-in-variables (EIV) model. However, its exact solution is rather complicated, and the accuracies of estimated parameters are too difficult to analytically compute. Since the EIV model is essentially a non-linear model, it can be solved according to the theory of non ...
Yunzhong Shen, Bofeng Li, Yi Chen
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Location and estimation of multiple outliers in weighted total least squares
Measurement, 2021Abstract Although the weighted total least squares (WTLS) adjustment is a rigorous method for estimating parameters in errors-in-variables (EIV) models, its solution is unreliable if the design matrix and/or observations contain multiple outliers.
Jianmin Wang +3 more
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A modified iterative algorithm for the weighted total least squares
Acta Geodaetica et Geophysica, 2020In this paper first, the method used for solving the weighted total least squares is discussed in two cases; (1) The parameter corresponding to the erroneous column in the design matrix is a scalar, model $$({\mathbf{H}} + {\mathbf{G}})^{T} {\mathbf{r}} + \delta \, = {\mathbf{q}} + {\mathbf{e}}$$, (2) The parameter corresponding to the erroneous column
Younes Naeimi, Behzad Voosoghi
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Bias-Corrected Weighted Total Least-Squares Adjustment of Condition Equations
Journal of Surveying Engineering, 2015AbstractThe total least-squares (TLS) method and its variations have recently received increasing research attention. However, little attention has been given to the weighted TLS adjustment method with condition equations. In this paper, a weighted TLS method designed for condition equations (WTLSC) is presented with the assumption that both the ...
Xiaohua Tong +4 more
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Locally weighted total least-squares variance component estimation for modeling urban air pollution
Environmental Monitoring and Assessment, 2022Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which ...
Arezoo Mokhtari, Behnam Tashayo
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On the Covariance Matrix of Weighted Total Least-Squares Estimates
Journal of Surveying Engineering, 2016AbstractThree strategies are employed to estimate the covariance matrix of the unknown parameters in an error-in-variable model. The first strategy simply computes the inverse of the normal matrix of the observation equations, in conjunction with the standard least-squares theory.
A. R. Amiri-Simkooei +2 more
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Jackknife resampling parameter estimation method for weighted total least squares
Communications in Statistics - Theory and Methods, 2019To make the result of weighted total least squares (WTLS) parameter estimation more accurate, the Jackknife method is used to resample the observed data and make full use of Jackknife samples for m...
Leyang Wang, Fengbin Yu
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