Confronta i metodi

	Regressione di Huber	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)	Stima MM per la regressione robusta	Regression with Ordinary Least Squares (OLS)
Campo	Statistica	Statistica	Statistica	Econometria
Famiglia	Regression model	Regression model	Regression model	Regression model
Anno di origine	1964	1984	1987	2019
Ideatore	Peter J. Huber	Peter J. Rousseeuw	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares
Tipo	Robust linear regression (M-estimation)	Robust linear regression	Robust linear regression	Linear regression
Fonte seminale	Huber, P. J. (1964). Robust Estimation of a Location Parameter. 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.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Correlati	5	5	5	5
Sintesi	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.	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.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).