Comparer des méthodes

	Moindres carrés ordinaires non linéaires (MCO non linéaires)	Moindres Carrés Généralisés (MCG)
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	1974–1987	1935
Auteur d'origine	Gallant (1987); Wooldridge (2010) for econometric treatment	Alexander Craig Aitken
Type	Nonlinear regression estimator	Linear estimator
Source fondatrice	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Alias	nonlinear least squares, NLS, NLLS, nonlinear regression	GLS, Aitken estimator, EGLS, feasible GLS
Apparentées	5	3
Résumé	Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.