Methoden vergleichen

	Robust Weighted Least Squares (Robust WLS)	Gewichtete Kleinste Quadrate (GKS)
Fachgebiet	Ökonometrie	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	1964/1981	1935
Urheber	Huber, P. J.	Alexander Craig Aitken
Typ	Robust weighted regression	Weighted linear estimator
Wegweisende Quelle	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Aliasnamen	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Verwandt	5	3
Zusammenfassung	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	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.