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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Comparing variable selection and model averaging methods for logistic regression.

Nikola Sekulovski1, František Bartoš1, Don van den Bergh1

  • 1Department of Psychology, University of Amsterdam, Amsterdam 1001 NK, The Netherlands.

Proceedings of the National Academy of Sciences of the United States of America
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

Bayesian model averaging (BMA) with specific priors excels in logistic regression without data separation. Penalized likelihood methods like LASSO are best when separation occurs, offering stable variable selection for binary outcomes.

Keywords:
logistic regressionmodel uncertaintyvariable selection

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Model uncertainty is a significant challenge in statistical modeling, particularly for binary outcomes like logistic regression.
  • The selection of appropriate predictors significantly impacts model performance and interpretation.
  • Existing methods for addressing model uncertainty in logistic regression lack comprehensive comparative performance data under realistic conditions.

Purpose of the Study:

  • To conduct a rigorous, simulation-based comparison of 28 established variable selection and inference methods for logistic regression.
  • To evaluate method performance across diverse empirical datasets with varying sample sizes and predictor numbers.
  • To identify the most effective strategies for handling model uncertainty in logistic regression, especially in the presence of data separation.

Main Methods:

  • A preregistered, simulation-based study comparing 28 methods for variable selection and inference.
  • Utilized 11 empirical datasets representing a range of sample sizes and predictor counts.
  • Assessed performance in scenarios both with and without data separation (a common issue in binary outcome modeling).

Main Results:

  • Bayesian model averaging (BMA) methods employing specific priors demonstrated superior performance when data separation was absent.
  • Penalized likelihood approaches, notably the LASSO (Least Absolute Shrinkage and Selection Operator), yielded the most stable results in the presence of separation.
  • BMA utilizing the empirical Bayes local (EB-local) prior proved competitive across both separated and non-separated datasets.

Conclusions:

  • BMA with specific priors offers strong performance for logistic regression when data separation is not an issue.
  • Penalized likelihood methods, particularly LASSO, are recommended for stable variable selection in logistic regression with data separation.
  • The EB-local prior for BMA provides a robust alternative, performing well in both separated and non-separated scenarios, offering practical guidance for applied researchers.