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...

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

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...

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

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

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

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

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

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

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...

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

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:...

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...

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

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...

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...

The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling...

{\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...

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

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...

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...

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...

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

including Bayesian regression and least squares fitting to variance stabilized responses, have been developed. Ordinary linear regression predicts the expected...

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

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

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

the many linear programming techniques (including the simplex method as well as others) can be applied. Iteratively re-weighted least squares Wesolowsky's...