Módszerek összehasonlítása

	Robuszt Súlyozott Legkisebb Négyzetek (Robuszt WLS)	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel	Robusztus általánosított legkisebb négyzetek (Robust GLS)
Tudományterület	Ökonometria	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1964/1981	2019	1936 / 1980
Megalkotó	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Típus	Robust weighted regression	Linear regression	Robust linear regression
Alapmű	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Alternatív nevek	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Kapcsolódó	5	5	5
Összefoglaló	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.