방법 비교

	Robust Regression	최소제곱법(OLS) 회귀
분야	통계학	계량경제학
계열	Regression model	Regression model
기원 연도	1964	2019
창시자	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Wooldridge (textbook treatment); classical least squares
유형	Regression with outlier resistance	Linear regression
원전	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
별칭	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
관련	6	5
요약	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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).