Comparer des méthodes

	Analyse Discriminante Robuste	Régression logistique
Domaine	Statistique	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1997	1958
Auteur d'origine	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	David Roxbee Cox
Type	Robust classification / discriminant analysis	Method
Source fondatrice	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi	logit model, binomial logistic regression, LR
Apparentées	5	3
Résumé	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).	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.