Confronta i metodi

	Regressione Robusta	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)	Regressione quantilica
Campo	Statistica	Statistica	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1964	1984	1978
Ideatore	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Peter J. Rousseeuw	Koenker & Bassett
Tipo	Regression with outlier resistance	Robust linear regression	Conditional quantile regression
Fonte seminale	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	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	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon
Correlati	6	5	5
Sintesi	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.