Comparer des méthodes

	Analyse robuste des classes latentes	Analyse de classes latentes (ACL)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2000s	1950s–1968
Auteur d'origine	Building on Hennig (2004) and Vermunt & Magidson (2004)	Paul F. Lazarsfeld
Type	Robust latent variable / mixture model	Latent variable / person-centered classification
Source fondatrice	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Apparentées	6	6
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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.