In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational...

Least squares is a mathematical optimization method that aims to determine the best fit function by minimizing the sum of the squares of the differences...

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems...

Least-squares adjustment is a model for the solution of an overdetermined system of equations based on the principle of least squares of observation residuals...

Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge...

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression;...

The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm:...

set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable...

Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum based on a least-squares fit of sinusoids to data samples, similar...

mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed...

called a left null vector of ⁠ A . {\displaystyle \mathbf {A} .} ⁠ A total least squares problem seeks the vector ⁠ x {\displaystyle \mathbf {x} } ⁠ that...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

classes of linear models, the total sum of squares equals the explained sum of squares plus the residual sum of squares. For proof of this in the multivariate...

general, total sum of squares = explained sum of squares + residual sum of squares. For a proof of this in the multivariate ordinary least squares (OLS)...

observations on both the x- and the y- axis. It is a special case of total least squares, which allows for any number of predictors and a more complicated...

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting...

means trying to minimize the orthogonal distance to the curve (e.g., total least squares), or to otherwise include both axes of displacement of a point from...

techniques, including principal component analysis, factor analysis, total least squares, latent semantic analysis, orthogonal regression, and dynamic mode...

when data are obtained in the presence of actuation. Total Least Squares DMD: Total Least Squares DMD is a recent modification of Exact DMD meant to address...

dissertation, Analysis of the Total Least Squares Problem and its use in parameter estimation, concerned total least squares methods for parameter estimation...

functional model or functional relationship. It can be corrected using total least squares and errors-in-variables models in general. The case that the x variable...

perpendicular distance to measure the quality of a fit exist, as in total least squares. The concept of perpendicular distance may be generalized to orthogonal...

External links 1.96 2SLS (two-stage least squares) – redirects to instrumental variable 3SLS – see three-stage least squares 68–95–99.7 rule 100-year flood...

Germany Terrestrial laser scanning, a 3D laser scanning method Total least squares, a statistical analysis Translesion synthesis, a form of DNA repair...

Polynomial regression models are usually fit using the method of least squares. The least-squares method minimizes the variance of the unbiased estimators of...

Mean square error Residual sum of squares Explained sum of squares Total sum of squares Scatterplot General linear model Ordinary least squares Generalized...

stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance...

fourth-order magic squares, while merely passing on the finished diagrams of larger squares. He gives a magic square of order 3, two squares for each order...

perpendicular distance to measure the quality of a fit exist, as in total least squares. The concept of perpendicular distance may be generalized to orthogonal...