Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| M-estimateurs (Régression Robuste)× | Régression quantile× | |
|---|---|---|
| Domaine≠ | Statistique | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2009 | 1978 |
| Auteur d'origine≠ | Peter J. Huber | Koenker & Bassett |
| Type≠ | Robust linear regression | Conditional quantile regression |
| Source fondatrice≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées | 5 | 5 |
| Résumé≠ | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit. | 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. |
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