Porovnať metódy

	Robustná jednoduchá lineárna regresia	Robustná viacnásobná lineárna regresia
Odbor	Štatistika	Štatistika
Rodina	Regression model	Regression model
Rok vzniku	1964-1987	1964–1980s
Tvorca	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Typ	Robust linear regression	Robust linear regression
Pôvodný zdroj	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.
Ďalšie názvy	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
Príbuzné	6	6
Zhrnutie	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.