Compară metode

	Regresia liniară multiplă robustă	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Statistică	Econometrie
Familie	Regression model	Regression model
Anul apariției	1964–1980s	2019
Autorul original	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Wooldridge (textbook treatment); classical least squares
Tip	Robust linear regression	Linear regression
Sursa seminală	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
Denumiri alternative	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	6	5
Rezumat	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).