Krahasoni metodat

	Katrorët më të vegjël të peshuar robust (Robust WLS)	Regresioni me Mënyrën më të Vogël të Katrorëve (OLS)	Regresioni kuantil	OLS Robust (OLS me Standard Errors Robustë)
Fusha	Ekonometri	Ekonometri	Ekonometri	Ekonometri
Familja	Regression model	Regression model	Regression model	Regression model
Viti i origjinës	1964/1981	2019	1978	1980
Krijuesi	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Halbert White
Lloji	Robust weighted regression	Linear regression	Conditional quantile regression	Linear regression with robust inference
Burimi themelues	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.
Emërtime të tjera	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
Të lidhura	5	5	5	6
Përmbledhja	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.