Bandingkan metode

	Weighted Least Squares (Robust WLS) yang Kuat	Generalized Least Squares (GLS) Robust (Robust GLS)	OLS Robust (OLS dengan Galat Standar Robust)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1964/1981	1936 / 1980	1980
Pencetus	Huber, P. J.	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Halbert White
Tipe	Robust weighted regression	Robust linear regression	Linear regression with robust inference
Sumber perintis	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Terkait	5	5	6
Ringkasan	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.	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.	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.