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

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...

of that for least squares. Least squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on...

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

methods for linear least squares entails the numerical analysis of linear least squares problems. A general approach to the least squares problem m i...

statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with...

In constrained least squares one solves a linear least squares problem with an additional constraint on the solution. This means, the unconstrained equation...

orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent...

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:...

damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve...

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

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...

global minimum of a sum of squares. For details concerning nonlinear data modeling see least squares and non-linear least squares. The assumption underlying...

; Hanson, Richard J. (1995). "23. Linear Least Squares with Linear Inequality Constraints". Solving Least Squares Problems. SIAM. p. 161. doi:10.1137/1...

Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms "least squares" and "linear model" are...

LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression. They address situations in which the classical...

{A} ^{\textsf {T}}} . Suppose that we wish to estimate a linear model using linear least squares. The model can be written as y = X β + ε , {\displaystyle...

In some cases, as in simple linear regression, the total sum of squares equals the sum of the two other sums of squares defined above: S S res + S S...

identified cluster is then subject to a verification procedure in which a linear least squares solution is performed for the parameters of the affine transformation...

method for training artificial neural networks. The simple example of linear least squares is used to explain a variety of ideas in online learning. The ideas...

are highly shape-flexible and can be parameterized with data using linear least squares (see Quantile-parameterized distribution#Transformations) The raised...

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...

number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is...

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...

is guaranteed and must be verified. Non-linear least squares may be also applied to the linear least squares problem by setting x 0 = 0 {\displaystyle...

Gauss–Newton algorithm is used to solve non-linear least squares problems, which is equivalent to minimizing a sum of squared function values. It is an extension...

It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition. It also includes...

Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function...

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...

ordinary least squares (OLS) estimator has the lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression...