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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.
Jian-min Wang +3 more
semanticscholar +2 more sources
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, B. Voosoghi
semanticscholar +2 more sources
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 multiple calculations. Combining Jackknife-1 and Jackknife-
Leyang Wang, Fengbin Yu
semanticscholar +2 more sources
Weighted total least-squares joint adjustment with weight correction factors
Communications in Statistics - Simulation and Computation, 2018A joint adjustment involves integrating different types of geodetic datasets, or multiple datasets of the same data type, into a single adjustment. This paper applies the weighted total least-squares (WTLS) principle to joint adjustment problems and ...
Leyang Wang, Hang Yu
semanticscholar +2 more sources
Survey Review, 2016
A mixed weighted least squares (WLS) and weighted total least squares (WTLS) (mixed WLS–WTLS) method is presented for an errors-in-variables (EIV) model with some fixed columns in the design matrix. The numerical computational scheme and an approximate accuracy assessment method are also provided.
Yongjun Zhou, Xing Fang
semanticscholar +2 more sources
A mixed weighted least squares (WLS) and weighted total least squares (WTLS) (mixed WLS–WTLS) method is presented for an errors-in-variables (EIV) model with some fixed columns in the design matrix. The numerical computational scheme and an approximate accuracy assessment method are also provided.
Yongjun Zhou, Xing Fang
semanticscholar +2 more sources
On weighted total least-squares for geodetic transformations
Journal of Geodesy, 2012In this contribution, it is proved that the weighted total least-squares (WTLS) approach preserves the structure of the coefficient matrix in errors-in-variables (EIV) model when based on the perfect description of the dispersion matrix. To achieve this goal, first a proper algorithm for WTLS is developed since the quite recent analytical solution for ...
V. Mahboub
semanticscholar +3 more sources
Weighted total least squares applied to mixed observation model
Survey Review, 2016This contribution presents the weighted total least squares (WTLS) formulation for a mixed errors-in-variables (EIV) model, generally consisting of two erroneous coefficient matrices and two erroneous observation vectors. The formulation is conceptually simple because it is formulated based on the standard least squares theory.
A. Amiri-Simkooei +2 more
semanticscholar +2 more sources
A robust weighted total least-squares solution with Lagrange multipliers
Survey Review, 2017Weighted total least-squares (WTLS) is becoming popular for parameter estimations in geodesy and surveying. However, it does not take into consideration the possible gross errors in observations, which may lead to a reduction in the robustness and reliability of parameter estimations. In order to solve this problem, in this study, Lagrange multipliers (
X. Gong, Z. Li
semanticscholar +2 more sources
Iterative algorithm for weighted total least squares adjustment
Survey Review, 2014In this contribution, an iterative algorithm is developed for parameter estimation in a nonlinear measurement error model y2e5(A2EA)x, which is based on the complete description of the variance–covariance matrices of the observation errors e and of the coefficient matrix errors EA without any restriction, e.g.
S. Jazaeri +2 more
semanticscholar +2 more sources