Comparer des méthodes

	Analyse robuste des classes latentes	Modélisation robuste de mélanges
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2000s	2000–2008
Auteur d'origine	Building on Hennig (2004) and Vermunt & Magidson (2004)	Peel & McLachlan (t-mixture); Garcia-Escudero et al. (trimming framework)
Type	Robust latent variable / mixture model	Latent-class probabilistic clustering with outlier protection
Source fondatrice	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Garcia-Escudero, L. A., Gordaliza, A., Matran, C. & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. Annals of Statistics, 36(3), 1324–1345.
Alias	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	robust mixture model, robust GMM, outlier-robust mixture model, trimmed mixture model
Apparentées	6	5
Résumé	Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.	Robust mixture modeling fits finite mixture models — probabilistic clustering methods that assume data arise from a blend of underlying subpopulations — using component distributions or estimation strategies designed to be insensitive to outliers and heavy-tailed noise. The two dominant approaches replace Gaussian components with heavier-tailed distributions such as the multivariate t, or trim a fixed proportion of the most extreme observations before fitting.