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Automated subset selection via information criteria optimization in generalized linear models

Author

Listed:
  • Benjamin Schwendinger

    (TU Wien
    Fraunhofer Austria Research GmbH)

  • Florian Schwendinger

    (University of Klagenfurt)

  • Laura Vana-Gür

    (TU Wien)

Abstract

In this paper, we show how mixed-integer conic programming can be used to directly optimize information criteria such as AIC and BIC in order to automate the model selection process for a collection of GLMs. Moreover, we propose to enhance the optimization problem with a novel linear constraint that limits pairwise correlation between the selected features and is well suited to tackle pairwise multicollinearity. Through a simulation study, we show that the proposed approach achieves high accuracy in selecting the active coefficients and outperforms naive enumeration methods aimed at subset selection.

Suggested Citation

  • Benjamin Schwendinger & Florian Schwendinger & Laura Vana-Gür, 2025. "Automated subset selection via information criteria optimization in generalized linear models," Computational Statistics, Springer, vol. 40(9), pages 5791-5831, December.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:9:d:10.1007_s00180-025-01672-9
    DOI: 10.1007/s00180-025-01672-9
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    References listed on IDEAS

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