Methoden vergleichen

	Robuste multinominale logistische Regression	Ordinal-Logistische Regression
Fachgebiet	Statistik	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	2001 (robust GLM); 1970s–1980s (multinomial logistic regression)	1980
Urheber	Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)	Peter McCullagh
Typ	Robust classification model	Ordinal regression / GLM
Wegweisende Quelle	Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.
Aliasnamen	robust polychotomous logistic regression, outlier-resistant multinomial regression, robust nominal logistic regression, M-estimation multinomial logistic regression	proportional-odds model, cumulative link model, ordered logit, OLR
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.	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.