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Contributions to Bayesian Structural Equation Modeling

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Séverine Demeyer

    (LNE, Laboratoire National de Métrologie et d’Essais
    Chaire de statistique appliquée & CEDRIC, CNAM)

  • Nicolas Fischer

    (LNE, Laboratoire National de Métrologie et d’Essais)

  • Gilbert Saporta

    (Chaire de statistique appliquée & CEDRIC, CNAM)

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.

Suggested Citation

  • Séverine Demeyer & Nicolas Fischer & Gilbert Saporta, 2010. "Contributions to Bayesian Structural Equation Modeling," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 469-476, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_46
    DOI: 10.1007/978-3-7908-2604-3_46
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