Сравнение методов

	Робастный взвешенный метод наименьших квадратов (Robust WLS)	Квантильная регрессия	Робастный обобщенный метод наименьших квадратов (Robust GLS)
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1964/1981	1978	1936 / 1980
Автор метода	Huber, P. J.	Koenker & Bassett	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Тип	Robust weighted regression	Conditional quantile regression	Robust linear regression
Основополагающий источник	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Другие названия	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	conditional quantile regression, regression quantiles, Kantil Regresyon	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Связанные	5	5	5
Сводка	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.	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 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.