Information criteria exploiting latent structure for model selection in Structural Equation Models

Structural equation models (SEM) are widely used to describe dependency structures between latent variables, making model selection a key issue in many applications.

Existing information criteria are generally based on the integrated observed-data likelihood and therefore do not explicitly account for the latent structure of the model.

In this paper, we propose two new information criteria derived from the integrated complete-data likelihood.

The first adapts the Integrated Completed Likelihood criterion to Gaussian SEM, while the second proposes an alternative approach to approximating the integrated observed-data log-likelihood by incorporating latent structural information and using an importance sampling strategy.

Their performance is assessed through an extensive simulation study covering null, direct, indirect and complete latent structures under different sample sizes and signal strengths.

The results show that the proposed importance sampling strategy provides robust and competitive model selection across a wide range of scenarios, whereas the proposed ICL criterion is particularly effective for recovering latent dependency structures when the latent variables are accurately estimated.

These findings demonstrate the potential benefits of explicitly exploiting the latent structure when developing information criteria for structural equation models.