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Double-bootstrap methods that use a single double-bootstrap simulation

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  • Jinyuan Chang
  • Peter Hall

Abstract

We show that, when the double bootstrap is used to improve performance of bootstrap methods for bias correction, techniques based on using a single double-bootstrap sample for each single-bootstrap sample can produce third-order accuracy for much less computational expense than is required by conventional double-bootstrap methods. However, this improved level of performance is not available for the single double-bootstrap methods that have been suggested to construct confidence intervals or distribution estimators.

Suggested Citation

  • Jinyuan Chang & Peter Hall, 2015. "Double-bootstrap methods that use a single double-bootstrap simulation," Biometrika, Biometrika Trust, vol. 102(1), pages 203-214.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:203-214.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu060
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    Cited by:

    1. Giuseppe Cavaliere & S'ilvia Gonc{c}alves & Morten {O}rregaard Nielsen & Edoardo Zanelli, 2022. "Bootstrap inference in the presence of bias," Papers 2208.02028, arXiv.org, revised Nov 2023.
    2. Antonio R. Linero, 2022. "Simulation‐based estimators of analytically intractable causal effects," Biometrics, The International Biometric Society, vol. 78(3), pages 1001-1017, September.
    3. Flores-Agreda, Daniel & Cantoni, Eva, 2019. "Bootstrap estimation of uncertainty in prediction for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 1-17.
    4. Simos G. Meintanis & Christos K. Papadimitriou, 2022. "Goodness--of--fit tests for stochastic frontier models based on the characteristic function," Journal of Productivity Analysis, Springer, vol. 57(3), pages 285-296, June.
    5. Davidson, Russell & Trokić, Mirza, 2020. "The fast iterated bootstrap," Journal of Econometrics, Elsevier, vol. 218(2), pages 451-475.

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