Vertaile menetelmiä

	Robust OLS (OLS, jossa robustit keskivirheet)	Painotettu pienimmän neliösumman menetelmä (WLS)
Tieteenala	Ekonometria	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1980	1935
Kehittäjä	Halbert White	Alexander Craig Aitken
Tyyppi	Linear regression with robust inference	Weighted linear estimator
Alkuperäislähde	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Rinnakkaisnimet	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Liittyvät	6	3
Tiivistelmä	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.	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.