Võrdle meetodeid

	Huberi regressioon	M-hinnangud (Robustne regressioon)	Tavaline vähimruutude (OLS) regressioon
Valdkond	Statistika	Statistika	Ökonomeetria
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1964	2009	2019
Looja	Peter J. Huber	Peter J. Huber	Wooldridge (textbook treatment); classical least squares
Tüüp	Robust linear regression (M-estimation)	Robust linear regression	Linear regression
Algallikas	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rööpnimetused	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Seotud	5	5	5
Kokkuvõte	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.	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to 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).