Methoden vergleichen

	Robuste multinominale logistische Regression	Robuste Regression
Fachgebiet	Statistik	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	2001 (robust GLM); 1970s–1980s (multinomial logistic regression)	1964
Urheber	Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Typ	Robust classification model	Regression with outlier resistance
Wegweisende Quelle	Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Aliasnamen	robust polychotomous logistic regression, outlier-resistant multinomial regression, robust nominal logistic regression, M-estimation multinomial logistic regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Verwandt	5	6
Zusammenfassung	Robust multinomial logistic regression extends the standard multinomial logit model to handle outliers, influential observations, and mild misspecification of the response distribution. It replaces the conventional maximum likelihood score equations with bounded influence functions (M-estimation) or pairs maximum likelihood with sandwich variance estimators, so that a small fraction of anomalous cases cannot distort the estimated log-odds ratios across outcome categories.	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.