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Related Experiment Videos

Random effects selection in linear mixed models.

Zhen Chen1, David B Dunson

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania, 625 Blockley Hall, 423 Guardian Dr. Philadelphia, Pennsylvania, USA. zchen@cceb.upenn.edu

Biometrics
|February 19, 2004
PubMed
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This study introduces a Bayesian method for selecting random effects in linear mixed models. It effectively identifies and removes random effects with zero variance, simplifying model selection for researchers.

Area of Science:

  • Statistics
  • Biostatistics
  • Computational Statistics

Background:

  • Selecting the appropriate random effects is crucial for accurate linear mixed model analysis.
  • Existing methods can be complex and computationally intensive.
  • Zero variance random effects can complicate model interpretation and inference.

Purpose of the Study:

  • To develop a robust and efficient method for selecting random effects in linear mixed models.
  • To identify and effectively remove random effects with zero variance.
  • To facilitate model simplification and improve interpretability.

Main Methods:

  • A hierarchical Bayesian model is employed to handle random effects selection.
  • Reparameterization of the mixed model incorporates covariance parameters as regression coefficients.

Related Experiment Videos

  • Mixture priors with point mass at zero are used to allow random effects to be excluded.
  • A computationally efficient Markov chain Monte Carlo (MCMC) algorithm is utilized for posterior computation.

    • Main Results:

    • The proposed method successfully identifies random effects with zero variance.
    • The reparameterization leads to a conditionally linear model structure, enabling the use of normal conjugate priors.
    • The MCMC algorithm provides efficient posterior computation.
    • The approach is validated using both simulated and real-world data.

    Conclusions:

    • The developed Bayesian approach offers a practical and efficient solution for random effects selection in linear mixed models.
    • This method simplifies model building by allowing the effective removal of non-influential random effects.
    • The approach is broadly applicable, as demonstrated by its use in analyzing environmental exposure and child development data.