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Prediction-based variable selection for component-wise gradient boosting

Author

Listed:
  • Potts Sophie

    (Chair of Spatial Data Science and Statistical Learning, University of Goettingen, Goettingen, Germany)

  • Bergherr Elisabeth

    (Chair of Spatial Data Science and Statistical Learning, University of Goettingen, Goettingen, Germany)

  • Reinke Constantin

    (Chair of Empirical Methods in Social Science and Demography, University of Rostock, Rostock, Germany)

  • Griesbach Colin

    (Chair of Spatial Data Science and Statistical Learning, University of Goettingen, Goettingen, Germany)

Abstract

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting. These approaches include Akaikes Information Criterion (AIC) as well as a selection rule relying on the component-wise test error computed via cross-validation. We implemented the AIC and cross-validation routines for Generalized Linear Models and evaluated them regarding their variable selection properties and predictive performance. An extensive simulation study revealed improved selection properties whereas the prediction error could be lowered in a real world application with age-standardized COVID-19 incidence rates.

Suggested Citation

  • Potts Sophie & Bergherr Elisabeth & Reinke Constantin & Griesbach Colin, 2024. "Prediction-based variable selection for component-wise gradient boosting," The International Journal of Biostatistics, De Gruyter, vol. 20(1), pages 293-314.
  • Handle: RePEc:bpj:ijbist:v:20:y:2024:i:1:p:293-314:n:1017
    DOI: 10.1515/ijb-2023-0052
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