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...

incorporated into the regression. WLS is also a specialization of generalized least squares, when all the off-diagonal entries of the covariance matrix of...

ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the...

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...

Numerical Methods for Least Squares Problems by Åke Björck (Chapter 4: Generalized Least Squares Problems.) Practical Least-Squares for Computer Graphics...

(or simply Gauss theorem for some authors) states that the ordinary least squares (OLS) estimator has the lowest sampling variance within the class of...

{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 \mathbf {y}...

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...

difficult to obtain discretizations, the moving least squares methods have also been used and generalized to solve PDEs on curved surfaces and other geometries...

Ecophysiology Evolutionary neurobiology Evolutionary physiology Generalized least squares (GLS) Generalized linear model Joe Felsenstein Mark Pagel Maximum likelihood...

the assumption of homoskedasticity is misleading. In that case, generalized least squares (GLS) was frequently used in the past. Nowadays, standard practice...

Gauss–Markov theorem. Linear least squares methods include mainly: Ordinary least squares Weighted least squares Generalized least squares Linear Template Fit...

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

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, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing...

least squares (OLS). Such estimates are consistent, however generally not as efficient as the SUR method, which amounts to feasible generalized least...

{\boldsymbol {\beta }}}\approx \mathbf {(J^{T}J)^{-1}J^{T}y} ,} compare generalized least squares with covariance matrix proportional to the unit matrix. The nonlinear...

efficiency as a result and makes it a special case of feasible generalized least squares. Consider the model y t = α + X t β + ε t , {\displaystyle y_{t}=\alpha...

bulb, a type of light bulb used for general lighting service Generalized least squares, in statistics Global location sensor Glutaminase, a gene and...

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...

functions, including maximum likelihood (ML), generalized least squares (GLS), and ordinary least squares (OLS), which are considered the "classical" discrepancy...

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

used for fitting linear regression with heteroscedastic errors Generalized least squares, used for fitting linear regression with correlated and/or heteroscedastic...

_{\infty }\mathbf {z} _{k},}} The Kalman filter can be derived as a generalized least squares method operating on previous data. Starting with our invariant...

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...

Least squares inference in phylogeny generates a phylogenetic tree based on an observed matrix of pairwise genetic distances and optionally a weight matrix...

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...

phylogenetic methods yielded wildly different numbers. Bayesian and generalized least-squares phylogenetic methods generated approximations of average group...

least squares, the R2 statistic can be calculated as above and may still be a useful measure. If fitting is by weighted least squares or generalized least...

in generalized least squares, compared to ordinary least squares, where P {\displaystyle P} is the identity matrix, and to weighted least squares, where...