Compara mètodes

	Regressió de Huber	Regressió per Mínims Quadrats Ordinàris (MQO)
Camp	Estadística	Econometria
Família	Regression model	Regression model
Any d'origen	1964	2019
Autor original	Peter J. Huber	Wooldridge (textbook treatment); classical least squares
Tipus	Robust linear regression (M-estimation)	Linear regression
Font seminal	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Àlies	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionats	5	5
Resum	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.	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).