Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастный взвешенный метод наименьших квадратов (Robust WLS)× | Робастный обобщенный метод наименьших квадратов (Robust GLS)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1964/1981 | 1936 / 1980 |
| Автор метода≠ | Huber, P. J. | Aitken (GLS theory, 1936); White (robust covariance, 1980) |
| Тип≠ | Robust weighted regression | Robust linear regression |
| Основополагающий источник≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | 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 | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS |
| Связанные | 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. | 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. |
| ScholarGateНабор данных ↗ |
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