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| Odporna regresja liniowa prosta× | Ważone Metody Najmniejszych Kwadratów (WLS)× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1964-1987 | 1935 |
| Twórca≠ | Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987) | Alexander Craig Aitken |
| Typ≠ | Robust linear regression | Weighted linear estimator |
| Źródło pierwotne≠ | Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339 | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Inne nazwy | robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Pokrewne≠ | 6 | 3 |
| Podsumowanie≠ | Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret. | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
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