Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Estimador S para Regresión Robusta× | Regresión Cuantílica× | |
|---|---|---|
| Campo≠ | Estadística | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1984 | 1978 |
| Autor original≠ | Rousseeuw & Yohai (1984) | Koenker & Bassett |
| Tipo≠ | Robust linear regression | Conditional quantile regression |
| Fuente seminal≠ | Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias | S-estimation, robust S-regression, S-Tahmin Edici | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relacionados | 5 | 5 |
| Resumen≠ | The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateConjunto de datos ↗ |
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