Comparer des méthodes

	Moindres Carrés Pondérés Non Linéaires (MCPNL)	Moindres Carrés Pondérés (MCP)
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	1960s–1980s (formalized in applied econometrics)	1935
Auteur d'origine	Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting	Alexander Craig Aitken
Type	Nonlinear regression estimator	Weighted linear estimator
Source fondatrice	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education. ISBN: 978-0134461366	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Alias	NWLS, nonlinear weighted least squares, weighted nonlinear regression, heteroscedasticity-corrected nonlinear regression	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Apparentées	3	3
Résumé	Nonlinear Weighted Least Squares combines the flexibility of nonlinear regression with the variance-stabilizing power of observation-level weights. It minimises a weighted sum of squared residuals around a user-specified nonlinear mean function, making it the method of choice when the relationship is inherently nonlinear and error variance differs across observations.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.