Comparer des méthodes

	Régression quantile robuste	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993–1997	2019
Auteur d'origine	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Wooldridge (textbook treatment); classical least squares
Type	Robust semiparametric regression	Linear regression
Source fondatrice	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	6	5
Résumé	Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).