Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Regrese nejmenších ořezaných čtverců (Least Trimmed Squares, LTS)× | MM-odhad pro robustní regresi× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1984 | 1987 |
| Tvůrce≠ | Peter J. Rousseeuw | Victor J. Yohai |
| Typ | Robust linear regression | Robust linear regression |
| Původní zdroj≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Další názvy≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. |
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