Are Bayesian regularization methods a must for multilevel dynamic latent variables models?

ABSTRACT: Due to the increased availability of intensive longitudinal data, researchers have been able to specify increasingly complex dynamic latent variable models. However, these models present challenges related to overfitting, hierarchical features, non-linearity, and sample size requirements. There are further limitations to be addressed regarding the finite sample performance of priors, including bias, accuracy, and type I error inflation. Bayesian estimation provides the flexibility to treat these issues simultaneously through the use of regularizing priors. In this paper, we aim to compare several Bayesian regularizing priors (ridge, Bayesian Lasso, adaptive spike-and-slab Lasso, and regularized horseshoe). To achieve this, we introduce a multilevel dynamic latent variable model. We then co

SUBMITTER: Andriamiarana VV 

PROVIDER: S-EPMC11754388 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature