Linganisha mbinu

	Robust Multiple linear regression	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Takwimu	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	1964–1980s	2019
Mwanzilishi	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Wooldridge (textbook treatment); classical least squares
Aina	Robust linear regression	Linear regression
Chanzo asilia	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
Majina mbadala	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	6	5
Muhtasari	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).