[D F12] Bayesian analysis of structural equation models using parameter expansion

Chapitres de Livre : Titre du livre: "Statistical learning and data science", January 2012, Chapman & Hall/CRC, pp. 135-145, (isbn: 978-1-4398-6763-1)

Mots clés: Bayesian statistics, structural equation models, parameter expansion, Gibbs sampler

Résumé: Structural Equation Models with latent variables (SEM) are hypothetical constructs used to represent causality relationships in data, where the observed correlation structure is transferred into the correlation structure of latent variables. In this paper a Bayesian analysis of SEM is proposed using parameter expansion to overcome identi fiability issues. An original use of posterior draws from latent variables is proposed to model expert knowledge in uncertainty analysis.

Equipe: msdma


@inbook {
D F12,
title="{Statistical learning and data science}",
chapter="{Bayesian analysis of structural equation models using parameter expansion}",
author="S. Demeyer and J. Foulley and N. Fischer and G. Saporta",
publisher="Chapman & Hall/CRC",