Comparer des méthodes

	Moindres Carrés Pondérés (MCP)	Test de White pour l'hétéroscédasticité
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1935	1980
Auteur d'origine	Alexander Craig Aitken	Halbert White
Type	Weighted linear estimator	General test for heteroskedasticity
Source fondatrice	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares	White's general heteroskedasticity test, White değişen varyans testi
Apparentées	3	3
Résumé	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.	The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.