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

Yi Jin1, Jinsong Chen1

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This summary is machine-generated.

This study introduces a new variational Bayesian expectation-maximization algorithm (VBEM) for efficient variable selection in structural equation modeling, balancing predictive accuracy and parsimony in psychological research.

Keywords:
MIMICVBEMVariable selectionpartially confirmatory

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Social Sciences

Background:

  • Variable selection is crucial in structural equation modeling for balancing predictive accuracy and parsimony.
  • Existing Bayesian regularization methods for sparsity are computationally intensive.
  • Markov chain Monte Carlo (MCMC) techniques limit the practical utility of current methods.

Purpose of the Study:

  • To propose a computationally efficient variational Bayesian expectation-maximization algorithm (VBEM) for variable selection.
  • To extend the multiple-indicators multiple-causes (MIMIC) model with enhanced variable selection capabilities.
  • To introduce a partially confirmatory framework for flexible incorporation of prior knowledge and regularization.

Main Methods:

  • Developed a variational Bayesian expectation-maximization (VBEM) algorithm.
  • Extended the multiple-indicators multiple-causes (MIMIC) model.
  • Implemented a partially confirmatory framework within the exploratory-confirmatory continuum.
  • Accounted for factor correlation in measurement and structural components.

Main Results:

  • The VBEM algorithm demonstrated flexibility and reliability in variable selection.
  • The proposed method proved efficient on both simulated and real datasets.
  • The partially confirmatory framework allowed for effective regularization and incorporation of substantive knowledge.

Conclusions:

  • The VBEM approach offers a computationally efficient alternative for variable selection in SEM.
  • The extended MIMIC model provides a flexible framework for complex data structures.
  • This method enhances parsimony and predictive accuracy in social and psychological studies.