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Comparing DIC and WAIC for multilevel models with missing data.

Han Du1, Brian Keller2, Egamaria Alacam3

  • 1Department of Psychology, UCLA, Los Angeles, CA, 90095, USA. hdu@psych.ucla.edu.

Behavior Research Methods
|October 20, 2023
PubMed
Summary
This summary is machine-generated.

Bayesian model assessment using Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) was compared. Marginal likelihood-based DIC showed the highest true model selection rates, especially with missing data.

Keywords:
Deviance information criterionMediation modelsMissing dataMultilevel modelingWatanabe-Akaike information criterion

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Area of Science:

  • Bayesian statistics
  • Statistical modeling

Background:

  • Deviance Information Criterion (DIC) and Watanabe-Aike Information Criterion (WAIC) are key Bayesian model assessment tools.
  • Comparing different versions of DIC and WAIC is crucial for accurate statistical inference.

Purpose of the Study:

  • To compare the performance of conditional and marginal DIC and WAIC.
  • To investigate the impact of missing data on these criteria.
  • To evaluate the necessity of including nuisance models for incomplete exogenous variables.

Main Methods:

  • Utilized a multilevel mediation model for comparative analysis.
  • Focused on two versions of DIC (, ) and one version of WAIC.
  • Simulated data to assess performance under various conditions, including missing data.

Main Results:

  • The performance comparison between , , and WAIC depended on the use of marginal or conditional likelihoods.
  • Including nuisance models for exogenous variables was contingent on the likelihood type.
  • Marginal likelihood-based , excluding covariate models, demonstrated superior true model selection rates.

Conclusions:

  • Marginal likelihood-based is a robust criterion for Bayesian model selection, particularly with missing data.
  • The choice between conditional and marginal approaches impacts the effectiveness of DIC and WAIC.
  • Excluding covariate models in marginal likelihood calculations can enhance model selection accuracy.