Confronta i metodi

	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)	Regressione quantilica
Campo	Statistica	Econometria
Famiglia	Regression model	Regression model
Anno di origine	1984	1978
Ideatore	Peter J. Rousseeuw	Koenker & Bassett
Tipo	Robust linear regression	Conditional quantile regression
Fonte seminale	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	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon
Correlati	5	5
Sintesi	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by 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.