Methoden vergelijken

	Robuuste enkelvoudige lineaire regressie	Robuuste meervoudige lineaire regressie
Vakgebied	Statistiek	Statistiek
Familie	Regression model	Regression model
Jaar van ontstaan	1964-1987	1964–1980s
Grondlegger	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Type	Robust linear regression	Robust linear regression
Oorspronkelijke bron	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. Annals of Mathematical Statistics, 35(1), 73–101.
Aliassen	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
Verwant	6	6
Samenvatting	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 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.