Confronta i metodi

	Stima MM per la regressione robusta	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)
Campo	Statistica	Statistica
Famiglia	Regression model	Regression model
Anno di origine	1987	1984
Ideatore	Victor J. Yohai	Peter J. Rousseeuw
Tipo	Robust linear regression	Robust linear regression
Fonte seminale	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Alias	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	LTS, least trimmed squares regression, trimmed least squares, robust regression
Correlati	5	5
Sintesi	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.	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.