Comparer des méthodes

	Régression par Moindres Médianes des Carrés (LMS)	Régression quantile
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1984	1978
Auteur d'origine	Peter J. Rousseeuw	Koenker & Bassett
Type	Robust linear regression	Conditional quantile regression
Source fondatrice	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	LMS, least median of squares regression, en küçük medyan kareler (LMS)	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5
Résumé	Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers.	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.