Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais svērtais mazāko kvadrātu metode (Robust WLS)× | Robustā vispārīgā mazāko kvadrātu metode (Robust GLS)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1964/1981 | 1936 / 1980 |
| Autors≠ | Huber, P. J. | Aitken (GLS theory, 1936); White (robust covariance, 1980) |
| Tips≠ | Robust weighted regression | Robust linear regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi | 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 |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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