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...

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

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

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

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...

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

generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there is a non-zero amount...

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

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

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

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

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

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

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...

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:...

{\displaystyle \psi } . Due to the non-linearity of the equation, numerical techniques such as the non-linear least-squares method can be used to solve the...

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...

different sizes and A {\displaystyle A} may be non-square. The standard approach is ordinary least squares linear regression.[clarification needed] However...

directly or by non-linear least squares. Then the more efficient linear regression can be used to predict q using c thus selecting the non-zero values in...

estimators for which the objective function is a sample average. Both non-linear least squares and maximum likelihood estimation are special cases of M-estimators...

signal, in biology Nonlinear Schrödinger equation, in physics Non-linear least squares, in statistics, a method used in regression analysis Nanosatellite...

packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some...

(X). Regression analysis Linear regression Least squares Linear least squares (mathematics) Non-linear least squares Least absolute deviations Curve...

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...

The most common methods use maximum likelihood estimation or non-linear least-squares estimation. Statistical model checking by testing whether the estimated...

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...

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...

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

Levenberg–Marquardt algorithm, used to solve non-linear least squares problems Leading monomial Linear Monolithic, a National Semiconductor prefix for...

predictand Weighted least squares, used for fitting linear regression with heteroscedastic errors Generalized least squares, used for fitting linear regression...