Bayesian model selection for structural equation models for myopia data

Tsai Hung Fan, Yi Fu Wang

研究成果: Article同行評審


Structural equation models (SEMs) are extensively used in behavioral, social, and psychological research to model relationships between latent variables and observations. However, most models include redundant variables that are not important to relationships within the model. This study uses a Bayesian model selection procedure with mixture priors for SEMs to determine variables that have a meaningful relationship. The posterior probabilities for all possible models are calculated through the MCMC algorithm and the model with the largest probability is selected. The predictors of non-zero fixed-effect coefficients and/or non-zero random-effect variance components in the MCMC procedure are determined without the need for complex model selection criteria. The proposed methodologies are illustrated in a simulation and applied to a multidimensional longitudinal myopia data set in which the dimensionality of the parameter space is reduced and a more parsimonious model is selected.

頁(從 - 到)5680-5694
期刊Communications in Statistics: Simulation and Computation
出版狀態Published - 2023

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬


深入研究「Bayesian model selection for structural equation models for myopia data」主題。共同形成了獨特的指紋。