Сравнение на методи

	Регресия на Хюбер	MM-оценка за робастна регресия	Метод на най-малките квадрати (МНК)
Област	Статистика	Статистика	Иконометрия
Семейство	Regression model	Regression model	Regression model
Година на възникване	1964	1987	2019
Създател	Peter J. Huber	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares
Тип	Robust linear regression (M-estimation)	Robust linear regression	Linear regression
Основополагащ източник	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Други названия	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Свързани	5	5	5
Резюме	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.	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.	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).