Comparer des méthodes

	Analyse robuste des classes latentes	Analyse robuste des profils latents
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2000s	2010s
Auteur d'origine	Building on Hennig (2004) and Vermunt & Magidson (2004)	Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)
Type	Robust latent variable / mixture model	Person-centered mixture model with robust estimation
Source fondatrice	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Vermunt, J. K. & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press. ISBN: 978-0521594035
Alias	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis
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 latent profile analysis identifies latent subgroups of individuals based on their continuous multivariate indicators while protecting parameter estimates from distortion by outliers or atypical observations. It extends standard latent profile analysis by replacing the Gaussian component densities with heavier-tailed or contaminated-normal alternatives that down-weight extreme cases during estimation.