Uporedite metode

	Робусни пондерисани метод најмањих квадрата (Robust WLS)	Regresija običnih najmanjih kvadrata (OLS)	Kvantilna regresija	Robusni OLS (OLS sa robusnim standardnim greškama)
Oblast	Ekonometrija	Ekonometrija	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model	Regression model	Regression model
Godina nastanka	1964/1981	2019	1978	1980
Tvorac	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Halbert White
Tip	Robust weighted regression	Linear regression	Conditional quantile 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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	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	conditional quantile regression, regression quantiles, Kantil Regresyon	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Srodne	5	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.