방법 비교

	강건 단순 선형 회귀	Robust Regression
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1964-1987	1964
창시자	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
유형	Robust linear regression	Regression with outlier resistance
원전	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
별칭	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
관련	6	6
요약	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	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.