Vertaile menetelmiä

	Robust Latent Class Analysis	Sekoitusmallinnus
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Latent structure	Latent structure
Syntyvuosi	2000s	1894
Kehittäjä	Building on Hennig (2004) and Vermunt & Magidson (2004)	Karl Pearson
Tyyppi	Robust latent variable / mixture model	Latent variable / density estimation
Alkuperäislähde	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Rinnakkaisnimet	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	finite mixture model, mixture distribution model, FMM, model-based clustering
Liittyvät	6	6
Tiivistelmä	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.	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.