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...

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

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

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

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

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...

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...

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...

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

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...

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:...

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

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

Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares...

value decomposition. Then the best rank‑k approximation to P in the least‑squares (Frobenius‑norm) sense is P k = U k Σ k V k T {\displaystyle P_{k}=U_{k}\...

Least trimmed squares (LTS), or least trimmed sum of squares, is a robust statistical method that fits a function to a set of data whilst not being unduly...

most notably limited information maximum likelihood and two-stage least squares. Suppose there are m regression equations of the form y i t = y − i...

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

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

correlated with the error term (endogenous), in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in...

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

Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM)...

In mathematics, least squares function approximation applies the principle of least squares to function approximation, by means of a weighted sum of other...

often assumed to be that which minimizes the sum of squared residuals. This is the ordinary least squares (OLS) approach. However, in cases where the dependent...

inverse column-updating method, the quasi-Newton least squares method and the quasi-Newton inverse least squares method. More recently quasi-Newton methods...

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

in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error. In the derivation of the RLS, the input...

version of the least squares cost function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Use of the Mean Squared Error (MSE) as...

be measured with two sums of squares formulas: The sum of squares of residuals, also called the residual sum of squares: S S res = ∑ i ( y i − f i ) 2...