Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Total $ M_{split} $ estimation
https://orcid.org/0000-0002-7069-7670
Abstract $ M_{split} $ estimation is a method that enables the estimation of mutually competing versions of parameters in functional observation models. In the presented study, the classical functional models found in it are replaced by errors-in-variables (EIV) models. Similar to the weighted total least-squares (WTLS) method, the random components of these models were assigned covariance matrix models. Thus, the proposed method, named Total $ M_{split} $ ($ TM_{split} $) estimation, corresponds to the basic rules of WTLS. $ TM_{split} $ estimation objective function is constructed using the components of squared $ M_{split} $ and WTLS estimation objective functions. The $ TM_{split} $ estimation algorithm is based on the Gauss–Newton method that is applied using a linear approximation of EIV models. The basic properties of the method are presented using examples of the estimation of regression line parameters and the estimation of parameters in a two-dimensional affine transformation.
Total $ M_{split} $ estimation
https://orcid.org/0000-0002-7069-7670
Abstract $ M_{split} $ estimation is a method that enables the estimation of mutually competing versions of parameters in functional observation models. In the presented study, the classical functional models found in it are replaced by errors-in-variables (EIV) models. Similar to the weighted total least-squares (WTLS) method, the random components of these models were assigned covariance matrix models. Thus, the proposed method, named Total $ M_{split} $ ($ TM_{split} $) estimation, corresponds to the basic rules of WTLS. $ TM_{split} $ estimation objective function is constructed using the components of squared $ M_{split} $ and WTLS estimation objective functions. The $ TM_{split} $ estimation algorithm is based on the Gauss–Newton method that is applied using a linear approximation of EIV models. The basic properties of the method are presented using examples of the estimation of regression line parameters and the estimation of parameters in a two-dimensional affine transformation.
Total $ M_{split} $ estimation
https://orcid.org/0000-0002-7069-7670
Abstract $ M_{split} $ estimation is a method that enables the estimation of mutually competing versions of parameters in functional observation models. In the presented study, the classical functional models found in it are replaced by errors-in-variables (EIV) models. Similar to the weighted total least-squares (WTLS) method, the random components of these models were assigned covariance matrix models. Thus, the proposed method, named Total $ M_{split} $ ($ TM_{split} $) estimation, corresponds to the basic rules of WTLS. $ TM_{split} $ estimation objective function is constructed using the components of squared $ M_{split} $ and WTLS estimation objective functions. The $ TM_{split} $ estimation algorithm is based on the Gauss–Newton method that is applied using a linear approximation of EIV models. The basic properties of the method are presented using examples of the estimation of regression line parameters and the estimation of parameters in a two-dimensional affine transformation.
Total $ M_{split} $ estimation
Wiśniewski, Zbigniew (Autor:in)
Journal of Geodesy ; 96
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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