Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión de Huber× | Regresión por Mínimos Cuadrados Recortados (LTS)× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1964 | 1984 |
| Autor original≠ | Peter J. Huber | Peter J. Rousseeuw |
| Tipo≠ | Robust linear regression (M-estimation) | Robust linear regression |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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