Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Huberova regrese× | Regrese nejmenších ořezaných čtverců (Least Trimmed Squares, LTS)× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1964 | 1984 |
| Tvůrce≠ | Peter J. Huber | Peter J. Rousseeuw |
| Typ≠ | Robust linear regression (M-estimation) | Robust linear regression |
| Původní zdroj≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Další názvy≠ | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit. | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. |
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