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[DFS10a] Contributions to Bayesian Structural Equation ModelingConférence Internationale avec comité de lecture : COMPSTAT'2010, 19th International Conference on Computational Statistics, Paris, August 2010, pp.469-476, Paris, france,
motcle:
Résumé:
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 identiability of parameters is studied. The latter
study shows that latent variables should be standardized in the nalysis to ensure identifiability. This heuristics is in fact introduced to deal with complex identiability
constraints. To illustrate the point, identiability constraints are calculated in a marketing application, in which posterior draws of the constraints are derived
from the posterior conditional distributions of parameters.
Equipe:
msdma
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