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Contributions to Bayesian Structural Equation Modeling

Séverine Demeyer 1 Nicolas Fischer 1 Gilbert Saporta 2
1 LNE - Laboratoire National de Métrologie et d'Essais [Trappes]
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Structural equation models (SEMs) are multivariate latent variable models used to model causality structures in data. A Bayesian estimation and validation of SEMs is proposed and identi ability of parameters is studied. The latter study shows that latent variables should be standardized in the nalysis to ensure identi fiability. This heuristics is in fact introduced to deal with complex identi ability constraints. To illustrate the point, identi ability constraints are calculated in a marketing application, in which posterior draws of the constraints are derived from the posterior conditional distributions of parameters.
Keywords : structural equation modeling Bayesian statistics Gibbs sam- pling latent variables identifiability
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Séverine Demeyer, Nicolas Fischer, Gilbert Saporta. Contributions to Bayesian Structural Equation Modeling. COMPSTAT'2010, 19th International Conference on Computational Statistics, Paris, Aug 2010, Paris, France. pp.469-476. ⟨hal-01125763⟩

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