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Parsimonious model selection using information theory: a modified selection rule.

Luke A Yates1, Shane A Richards1, Barry W Brook1

  • 1School of Natural Sciences, University of Tasmania, Hobart, Tasmania, 7005, Australia.

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|July 17, 2021
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Summary
This summary is machine-generated.

Overfitting in ecological model selection is often due to score estimation uncertainty, not the criterion itself. A new rule, modifying the one-standard-error rule, accounts for this uncertainty to select more parsimonious and interpretable models.

Keywords:
cross validationinformation theorymodel selectionoverfittingparsimonypost-selection inference

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Area of Science:

  • Ecology
  • Statistics
  • Information Theory

Background:

  • Information-theoretic approaches like Akaike's Information Criterion (AIC) and cross-validation are standard for ecological model selection.
  • Overfitting remains a significant challenge, potentially undermining the interpretation of ecological processes.
  • Selection uncertainty in score estimation, rather than the criterion choice, is the primary driver of overfitting.

Purpose of the Study:

  • Introduce a novel model selection rule to address overfitting in ecological studies.
  • Develop a method that directly accounts for estimation uncertainty in model selection.
  • Improve the inferential properties of selected ecological models.

Main Methods:

  • Introduce a modified one-standard-error rule for model selection.
  • Incorporate direct accounting for estimation uncertainty into the selection process.
  • Illustrate the rule's application using maximum-likelihood estimation and Kullback-Leibler discrepancy.

Main Results:

  • The novel rule mitigates overfitting by directly addressing estimation uncertainty.
  • The proposed method reduces the inclusion of spurious effects in selected models.
  • Enhanced inferential properties are achieved through improved model parsimony.

Conclusions:

  • The new selection rule offers a more robust approach to ecological model selection.
  • Accounting for estimation uncertainty is crucial for reliable model inference.
  • The rule is broadly applicable, including in Bayesian model selection contexts.