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BIC extensions for order-constrained model selection.

J Mulder1,2, A E Raftery3

  • 1Tilburg University, The Netherlands.

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|July 11, 2022
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Summary
This summary is machine-generated.

This study introduces improved Bayesian Information Criteria (BIC) for models with order constraints, enhancing model selection accuracy in social sciences. The new methods, particularly the local unit information prior, offer better performance and lower error rates.

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Area of Science:

  • Social Sciences
  • Statistics
  • Econometrics

Background:

  • The Bayesian Information Criterion (BIC) is a standard tool for model selection in social sciences.
  • However, standard BIC is not suitable for models with order-constrained parameters.

Purpose of the Study:

  • To explore extensions of the BIC for evaluating order-constrained models.
  • To introduce a more accurate method for model selection in the presence of parameter order constraints.

Main Methods:

  • Developed two extensions of the BIC using truncated unit information priors for order-constrained models.
  • Investigated priors centered around maximum likelihood estimates and null values.
  • Implemented the superior method in the R package 'BFpack'.

Main Results:

  • The order-constrained BIC utilizing a local unit information prior demonstrated superior performance.
  • This method functions effectively as an Occam's razor for order-constrained models.
  • It resulted in significantly lower error probabilities compared to existing methods.

Conclusions:

  • The proposed methodology, based on the local unit information prior, enhances model selection for order-constrained models.
  • 'BFpack' package facilitates the application of this advanced method in research.
  • The approach is validated using data from the European Values Study.