Сравнение методов

	Робастная мультиномиальная логистическая регрессия	Робастная логистическая регрессия
Область	Статистика	Статистика
Семейство	Regression model	Regression model
Год появления	2001 (robust GLM); 1970s–1980s (multinomial logistic regression)	2001
Автор метода	Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)	Cantoni & Ronchetti (2001); Bondell (2008)
Тип	Robust classification model	Robust generalized linear model (binary outcome)
Основополагающий источник	Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.
Другие названия	robust polychotomous logistic regression, outlier-resistant multinomial regression, robust nominal logistic regression, M-estimation multinomial logistic regression	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon
Связанные	5	5
Сводка	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 Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).