Bayesian Structural Equation Envelope Model.

Abstract

The envelope model has gained significant attention since its proposal, offering a fresh perspective on dimension reduction in multivariate regression models and improving estimation efficiency. One of its appealing features is its adaptability to diverse regression contexts. This article introduces the integration of envelope methods into the factor analysis model. In contrast to previous research primarily focused on the frequentist approach, the study proposes a Bayesian approach for estimation and envelope dimension selection. A Metropolis-within-Gibbs sampling algorithm is developed to draw posterior samples for Bayesian inference. A simulation study is conducted to illustrate the effectiveness of the proposed method. Additionally, the proposed methodology is applied to the ADNI dataset to explore the relationship between cognitive decline and the changes occurring in various brain regions. This empirical application further highlights the practical utility of the proposed model in real-world scenarios.