Regularized Variational Bayesian Approximations for Variable Selection in Extended Multiple-Indicators Multiple-Causes Models

Dados Bibliográficos

ID19291318
AutoresYi Jin (0000-0002-4936-3596, University of Hong Kong), Jinsong Chen (0000-0002-0157-5469, University of Hong Kong, autor correspondente)
Ano2025
Volume60
Fascículo5
Páginas859-877
Data de publicação2025-09-03
Open AccessNão
TipoARTICLE
PeriódicoMultivariate Behavioral Research (JOURNAL)
Identificadores do periódicoISSN: 0027-3171 • E-ISSN: 1532-7906
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2025.2483253
PMID40208625
OpenAlexW4409316227
IdiomaEN
Referências citadas31

Variable selection in structural equation modeling has merged as a new concern in social and psychological studies. Researchers often aim to strike a balance between achieving predictive accuracy and fostering parsimonious explanations by identifying the most informative variables. While recent developments in Bayesian regularization methods offer promising solutions to promote model sparsity with much fewer "active" variables, their computational burden due to reliance on the Markov chain Monte Carlo technique limits practical utility. In response, this study proposes a variational Bayesian expectation-maximum algorithm (VBEM) for variable selection to extend the multiple-indicators multiple-causes (MIMIC) model. On the basis of traditional MIMIC models, a partially confirmatory framework that operates within the exploratory-confirmatory continuum is introduced, allowing for the flexible incorporation of substantive knowledge and regularization into both measurement and structural parts while accounting for factor correlation. The proposed method demonstrated its flexibility, reliability, and efficiency on both simulated and real data

Bayesian probability · Econometrics · Feature selection · Machine learning · Model selection · Selection (genetic algorithm) · Variable (mathematics) · Advanced Statistical Methods and Models · Applied Mathematics · Artificial Intelligence · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference