Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Régression Lasso	Régression quantile
Domaine	Économétrie	Apprentissage automatique	Économétrie
Famille	Regression model	Machine learning	Regression model
Année d'origine	2019	1996	1978
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.	Koenker & Bassett
Type	Linear regression	Regularized linear regression (L1 penalty)	Conditional quantile regression
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	4	5
Résumé	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).	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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.