Usporedite metode

	Robusni ponderirani najmanji kvadrati (Robust WLS)	Regresija običnih najmanjih kvadrata (OLS)	Robustni OLS (OLS s robustnim standardnim pogreškama)
Područje	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model
Godina nastanka	1964/1981	2019	1980
Tvorac	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Halbert White
Vrsta	Robust weighted regression	Linear regression	Linear regression with robust inference
Temeljni izvor	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	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Drugi nazivi	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Srodne	5	5	6
Sažetak	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 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.