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| Regressione Lineare Semplice Robusta× | Regressione quantilica× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1964-1987 | 1978 |
| Ideatore≠ | Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987) | Koenker & Bassett |
| Tipo≠ | Robust linear regression | Conditional quantile regression |
| Fonte seminale≠ | Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret. | 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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