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

Andrea C. Garcia-Angulo () and Gerda Claeskens ()
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Andrea C. Garcia-Angulo: Escuela Superior Politécnica del Litoral, ESPOL
Gerda Claeskens: KU Leuven

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 3, No 2, 340 pages

Abstract: 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.

Keywords: Bootstrap; Likelihood model; Misspecification; Model averaging; Post-selection inference (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00184-024-00956-2

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