Сравнение методов

	Робастный латентно-кластерный анализ	Робастный эксплораторный факторный анализ
Область	Статистика	Психометрия
Семейство	Latent structure	Latent structure
Год появления	2000s	2000–2003
Автор метода	Building on Hennig (2004) and Vermunt & Magidson (2004)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Тип	Robust latent variable / mixture model	Latent variable / dimension reduction (robust)
Основополагающий источник	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
Другие названия	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Связанные	6	4
Сводка	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 exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.