Contributions to Bayesian Structural Equation Modeling

Séverine Demeyer (), Nicolas Fischer and Gilbert Saporta
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Séverine Demeyer: LNE, Laboratoire National de Métrologie et d’Essais
Nicolas Fischer: LNE, Laboratoire National de Métrologie et d’Essais
Gilbert Saporta: Chaire de statistique appliquée & CEDRIC, CNAM

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 469-476 from Springer

Abstract: 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 identifiability of parameters is studied. The latter study shows that latent variables should be standardized in the analysis to ensure identifiability. This heuristics is in fact introduced to deal with complex identifiability constraints. To illustrate the point, identifiability 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 sampling; latent variables; identifiability (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_46

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DOI: 10.1007/978-3-7908-2604-3_46

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