Salīdzināt metodes

	Robustā multinomiālā logistiskā regresija	Vispārīgais lineārais modelis (GLM)
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	2001 (robust GLM); 1970s–1980s (multinomial logistic regression)	1972
Autors	Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)	John A. Nelder & Robert W. M. Wedderburn
Tips	Robust classification model	Regression framework
Pirmavots	Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Citi nosaukumi	robust polychotomous logistic regression, outlier-resistant multinomial regression, robust nominal logistic regression, M-estimation multinomial logistic regression	GLM, generalized regression, exponential family regression, link-function model
Saistītās	5	6
Kopsavilkums	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.	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.