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Bootstrap Approximation of Model Selection Probabilities for Multimodel Inference Frameworks.

Andres Dajles1, Joseph Cavanaugh1

  • 1Department of Biostatistics, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
Summary
This summary is machine-generated.

Statistical model selection can be biased. This study corrects bootstrap bias in model selection probabilities and shows Akaike weights are poor surrogates for these probabilities, though useful for model plausibility.

Keywords:
Akaike information criterionAkaike weightsBayesian model averagingbootstrappingmodel selection

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Area of Science:

  • Statistics
  • Statistical Modeling
  • Model Selection

Background:

  • Statistical modeling often involves selecting from a collection of candidate models.
  • Information criteria balance data fidelity and parsimony, but ignoring selection variability leads to flawed inferences.
  • Multimodel frameworks address modeling uncertainty, ideally using model selection probabilities.

Purpose of the Study:

  • To investigate bias in bootstrap approximations of model selection probabilities.
  • To propose a bias correction for bootstrap-based model selection probabilities.
  • To evaluate Akaike weights as surrogates for model selection probabilities.

Main Methods:

  • Utilized bootstrapping to approximate model selection probabilities.
  • Introduced a bias correction method for bootstrap approximations.
  • Compared bootstrap-approximated probabilities with Akaike weights.

Main Results:

  • The conventional bootstrap approach for approximating model selection probabilities is shown to be biased.
  • A simple correction is proposed and demonstrated to adjust for this bias.
  • Akaike weights are found to be inadequate approximations of selection probabilities.

Conclusions:

  • Accounting for model selection uncertainty is crucial for valid statistical inference.
  • The proposed bootstrap correction improves the accuracy of model selection probabilities.
  • While useful for assessing model plausibility, Akaike weights should not be used as direct surrogates for selection probabilities.