Jämför metoder

	Robust Latent Class Analysis	Robust latentprofilanalys
Ämnesområde	Statistik	Statistik
Familj	Latent structure	Latent structure
Ursprungsår	2000s	2010s
Upphovsperson	Building on Hennig (2004) and Vermunt & Magidson (2004)	Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)
Typ	Robust latent variable / mixture model	Person-centered mixture model with robust estimation
Ursprungskälla	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
Närliggande	6	5
Sammanfattning	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.