Comparer des méthodes

	Régression quantile (variantes non paramétriques)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978	2019
Auteur d'origine	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares
Type	Quantile regression (nonparametric variants)	Linear regression
Source fondatrice	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
Résumé	Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.	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).