Abstract:In classical regression analysis, the error of independent variable is usually not taken into account in regression analysis. This paper presents two solution methods for the case that both the independent and the dependent variables have errors. These methods are derived from the condition-adjustment and indirect-adjustment models based on the Total-Least-Squares principle. The equivalence of these two methods is also proven in theory
The percentage error, or error relative to the observed value is usually felt to be more meaningful ...
A linear problem of regression analysis is considered under the assumption of the presence of noise ...
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...
In classical regression analysis, the error of independent variable is usually not taken into accoun...
In the present thesis we deal with the linear regression models based on least squares. These method...
summary:Orthogonal regression, also known as the total least squares method, regression with errors-...
AbstractThe total least-squares (TLS) technique, which is well known in numerical linear algebra and...
In this work we study the least squares and the total least squares problem for the solution of line...
When the total least squares (TLS) solution is used to solve the parameters in the errors-in-variabl...
Least-Squares (LS) adjustment method aims at estimating a vector of parameters ξ, from a linear mode...
A new regression model which mininizes the sum of squares of relative residues for data with errors ...
This paper proposes the universal errors-in-variables (EIV) adjustment model based on the fundamenta...
The paper presents an estimator of the errors-in-variables in multiple regressions using only first ...
Much of the data analysed by least squares regression methods violates the assumption that independe...
Fitting a surface to a given set of measurements is an essential function for engineers and geodesis...
The percentage error, or error relative to the observed value is usually felt to be more meaningful ...
A linear problem of regression analysis is considered under the assumption of the presence of noise ...
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...
In classical regression analysis, the error of independent variable is usually not taken into accoun...
In the present thesis we deal with the linear regression models based on least squares. These method...
summary:Orthogonal regression, also known as the total least squares method, regression with errors-...
AbstractThe total least-squares (TLS) technique, which is well known in numerical linear algebra and...
In this work we study the least squares and the total least squares problem for the solution of line...
When the total least squares (TLS) solution is used to solve the parameters in the errors-in-variabl...
Least-Squares (LS) adjustment method aims at estimating a vector of parameters ξ, from a linear mode...
A new regression model which mininizes the sum of squares of relative residues for data with errors ...
This paper proposes the universal errors-in-variables (EIV) adjustment model based on the fundamenta...
The paper presents an estimator of the errors-in-variables in multiple regressions using only first ...
Much of the data analysed by least squares regression methods violates the assumption that independe...
Fitting a surface to a given set of measurements is an essential function for engineers and geodesis...
The percentage error, or error relative to the observed value is usually felt to be more meaningful ...
A linear problem of regression analysis is considered under the assumption of the presence of noise ...
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...