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Bootstrap quantile estimation via importance resampling

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  • Hu, Jiaqiao
  • Su, Zheng

Abstract

We propose an adaptive importance resampling algorithm for estimating bootstrap quantiles of general statistics. The algorithm is especially useful in estimating extreme quantiles and can be easily used to construct bootstrap confidence intervals. Empirical results on real and simulated data sets show that the proposed algorithm is not only superior to the uniform resampling approach, but may also provide more than an order of magnitude of computational efficiency gains.

Suggested Citation

  • Hu, Jiaqiao & Su, Zheng, 2008. "Bootstrap quantile estimation via importance resampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5136-5142, August.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:12:p:5136-5142
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    References listed on IDEAS

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    1. Rubinstein, Reuven Y., 1997. "Optimization of computer simulation models with rare events," European Journal of Operational Research, Elsevier, vol. 99(1), pages 89-112, May.
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    Cited by:

    1. Zhou, Hua & Lange, Kenneth, 2011. "A fast procedure for calculating importance weights in bootstrap sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 26-33, January.
    2. Shi Yang & Shi Weiping & Wang Mengqiao & Lee Ji-Hyun & Kang Huining & Jiang Hui, 2023. "Accurate and fast small p-value estimation for permutation tests in high-throughput genomic data analysis with the cross-entropy method," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 22(1), pages 1-22, January.

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