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...

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

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

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:...

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

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...

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...

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

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

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

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...

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

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

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...

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...

A few examples are linear least squares, smoothing splines, regression splines, local regression, kernel regression, and linear filtering. When the weights...

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...

has simple closed forms, and can be parameterized with data using linear least squares. The Marchenko–Pastur distribution is important in the theory of...

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...

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...

Piecewise linear function Linear approximation Linear interpolation Discontinuous linear map Linear least squares "The term linear function means a linear form...

the pseudoinverse is to compute a "best fit" (least squares) approximate solution to a system of linear equations that lacks an exact solution (see below...

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

\,} This is already a simple general linear model, and it can be estimated for example by ordinary least squares. Unfortunately, the task of decomposing...

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...