Comparer des méthodes

	Modèle robuste de mélange gaussien	Régression linéaire robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	1964–1987
Auteur d'origine	Peel, D. & McLachlan, G. J.	Huber, P. J.; Rousseeuw, P. J.
Type	Probabilistic clustering / density estimation	Outlier-resistant supervised regression
Source fondatrice	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Alias	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Apparentées	5	5
Résumé	Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.