Salīdzināt metodes

	Robustā latento klašu analīze	Robusta latentā profila analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2000s	2010s
Autors	Building on Hennig (2004) and Vermunt & Magidson (2004)	Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)
Tips	Robust latent variable / mixture model	Person-centered mixture model with robust estimation
Pirmavots	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
Citi nosaukumi	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
Saistītās	6	5
Kopsavilkums	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.