Comparer des méthodes

	Modélisation par mélange	Modélisation par équations structurelles
Domaine	Statistique	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1894	1921
Auteur d'origine	Karl Pearson	Sewall Wright
Type	Latent variable / density estimation	Method
Source fondatrice	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Alias	finite mixture model, mixture distribution model, FMM, model-based clustering	SEM, path analysis, latent variable modeling, causal modeling
Apparentées	6	3
Résumé	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.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.