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Optimal Bootstrap Sample Size in Construction of Percentile Confidence Bounds

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  • Kam‐Hin Chung
  • Stephen M. S. Lee

Abstract

In traditional bootstrap applications the size of a bootstrap sample equals the parent sample size, n say. Recent studies have shown that using a bootstrap sample size different from n may sometimes provide a more satisfactory solution. In this paper we apply the latter approach to correct for coverage error in construction of bootstrap confidence bounds. We show that the coverage error of a bootstrap percentile method confidence bound, which is of order O(n−2/2) typically, can be reduced to O(n−1) by use of an optimal bootstrap sample size. A simulation study is conducted to illustrate our findings, which also suggest that the new method yields intervals of shorter length and greater stability compared to competitors of similar coverage accuracy.

Suggested Citation

  • Kam‐Hin Chung & Stephen M. S. Lee, 2001. "Optimal Bootstrap Sample Size in Construction of Percentile Confidence Bounds," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 225-239, March.
    • Handle: RePEc:bla:scjsta:v:28:y:2001:i:1:p:225-239
    DOI: 10.1111/1467-9469.00233
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    Cited by:

    1. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    2. R. Lombardo & J.-F. Durand & A. Faraj, 2011. "Iterative Design of Experiments by Non-Linear PLS Models. A Case Study: The Reservoir Simulator Data to Forecast Oil Production," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 113-125, April.
    3. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael & Önkal, Dilek, 2019. "Judgmental adjustments through supply integration for strategic partnerships in food chains," Omega, Elsevier, vol. 87(C), pages 20-33.
    4. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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