Сравнение методов

	Робастный дискриминантный анализ	Робастная логистическая регрессия
Область	Статистика	Статистика
Семейство	Regression model	Regression model
Год появления	1997	2001
Автор метода	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	Cantoni & Ronchetti (2001); Bondell (2008)
Тип	Robust classification / discriminant analysis	Robust generalized linear model (binary outcome)
Основополагающий источник	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.
Другие названия	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon
Связанные	5	5
Сводка	Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).	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).