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Bootstrap for inference after model selection and model averaging for likelihood models

Author

Listed:
  • Andrea C. Garcia-Angulo

    (Escuela Superior Politécnica del Litoral, ESPOL)

  • Gerda Claeskens

    (KU Leuven)

Abstract

A one-step semiparametric bootstrap procedure is constructed to estimate the distribution of estimators after model selection and of model averaging estimators with data-dependent weights. The method is generally applicable to non-normal models. Misspecification is allowed for all candidate parametric models. The semiparametric bootstrap estimator is shown to be consistent within specific regions such that the good and the bad candidate models are separated. Simulation studies exemplify that the bootstrap procedure leads to short confidence intervals with a good coverage.

Suggested Citation

  • Andrea C. Garcia-Angulo & Gerda Claeskens, 2025. "Bootstrap for inference after model selection and model averaging for likelihood models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(3), pages 311-340, April.
    • Handle: RePEc:spr:metrik:v:88:y:2025:i:3:d:10.1007_s00184-024-00956-2
    DOI: 10.1007/s00184-024-00956-2
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    References listed on IDEAS

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