השוואת שיטות

	רגרסיית הובר	רגרסיית ריבועים זעירים חתוכים (Least Trimmed Squares - LTS)
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Regression model	Regression model
שנת המקור	1964	1984
הוגה השיטה	Peter J. Huber	Peter J. Rousseeuw
סוג	Robust linear regression (M-estimation)	Robust linear regression
מקור מכונן	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.
כינויים	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression
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.	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.