Methoden vergelijken

	Robuuste regressie	Gewogen Kleinste Kwadraten (GKK)
Vakgebied	Statistiek	Statistiek
Familie	Regression model	Regression model
Jaar van ontstaan	1964	1935
Grondlegger	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Alexander Craig Aitken
Type	Regression with outlier resistance	Weighted linear estimator
Oorspronkelijke bron	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Aliassen	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Verwant	6	3
Samenvatting	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.