השוואת שיטות

	רגרסיית הובר	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	סטטיסטיקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1964	2019
הוגה השיטה	Peter J. Huber	Wooldridge (textbook treatment); classical least squares
סוג	Robust linear regression (M-estimation)	Linear regression
מקור מכונן	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
כינויים	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	5	5
תקציר	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).