مقایسهٔ روش‌ها

	رگرسیون هیوبر	رگرسیون حداقل مربعات هرس‌شده (LTS)
حوزه	آمار	آمار
خانواده	Regression model	Regression model
سال پیدایش	1964	1984
پدیدآور	Peter J. Huber	Peter J. Rousseeuw
نوع	Robust linear regression (M-estimation)	Robust linear regression
منبع بنیادین	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.
نام‌های دیگر	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression
مرتبط	5	5
خلاصه	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.