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On overfitting and post-selection uncertainty assessments

Author

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  • L Hong
  • T A Kuffner
  • R Martin

Abstract

Summary In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected submodel, may not be valid because it ignores the selected submodel’s dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.

Suggested Citation

  • L Hong & T A Kuffner & R Martin, 2018. "On overfitting and post-selection uncertainty assessments," Biometrika, Biometrika Trust, vol. 105(1), pages 221-224.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:1:p:221-224.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx083
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    Cited by:

    1. Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals," Statistical Papers, Springer, vol. 62(5), pages 2407-2431, October.
    2. D García Rasines & G A Young, 2023. "Splitting strategies for post-selection inference," Biometrika, Biometrika Trust, vol. 110(3), pages 597-614.
    3. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).

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