Compara mètodes

	Regressió de Huber	Regressió quantílica
Camp	Estadística	Econometria
Família	Regression model	Regression model
Any d'origen	1964	1978
Autor original	Peter J. Huber	Koenker & Bassett
Tipus	Robust linear regression (M-estimation)	Conditional quantile regression
Font seminal	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Àlies	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionats	5	5
Resum	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.	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.