Сравнение методов

	Робастная множественная линейная регрессия	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Статистика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1964–1980s	2019
Автор метода	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Wooldridge (textbook treatment); classical least squares
Тип	Robust linear regression	Linear regression
Основополагающий источник	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
Другие названия	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	6	5
Сводка	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.	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).