Confronta i metodi

	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)	Stima basata sulla deviazione assoluta mediana (MAD)	ANOVA robusta (media troncata e di Welch)
Campo	Statistica	Statistica	Statistica
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1984	1974	1951
Ideatore	Peter J. Rousseeuw	Hampel (influence-curve treatment); classical robust statistics	Welch (1951); robust trimmed-mean approach popularised by Wilcox
Tipo	Robust linear regression	Robust scale estimator	Robust one-way analysis of variance
Fonte seminale	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.
Alias	LTS, least trimmed squares regression, trimmed least squares, robust regression	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean)
Correlati	5	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.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.	Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.