Methoden vergelijken

	Robuuste Latente Profielanalyse	Mixture Modeling
Vakgebied	Statistiek	Statistiek
Familie	Latent structure	Latent structure
Jaar van ontstaan	2010s	1894
Grondlegger	Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)	Karl Pearson
Type	Person-centered mixture model with robust estimation	Latent variable / density estimation
Oorspronkelijke bron	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	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Aliassen	RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis	finite mixture model, mixture distribution model, FMM, model-based clustering
Verwant	5	6
Samenvatting	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.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.