Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing

Xia Yemao and Pan Maolin

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 9, 4599-4619

Abstract: Confirmatory factor analysis (CFA) model is a useful multivariate statistical tool for interpreting relationships between latent variables and manifest variables. Often statistical results based on a single CFA are seriously distorted when data set takes on heterogeneity. To address the heterogeneity resulting from the multivariate responses, we propose a Bayesian semiparametric modeling for CFA. The approach relies on using a prior over the space of mixing distributions with finite components. Blocked Gibbs sampler is implemented to cope with the posterior analysis. Results obtained from a simulation study and a real data set are presented to illustrate the methodology.

Date: 2017
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DOI: 10.1080/03610926.2015.1083110

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