Bootstrap quantile estimation via importance resampling

Jiaqiao Hu; Zheng Su

doi:10.1016/j.csda.2008.05.022

Bootstrap quantile estimation via importance resampling

Jiaqiao Hu
, Zheng Su

Applied Mathematics and Statistics

Stony Brook University

Genentech, Inc

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English
Pages (from-to)	5136-5142
Number of pages	7
Journal	Computational Statistics and Data Analysis
Volume	52
Issue number	12
DOIs	https://doi.org/10.1016/j.csda.2008.05.022
State	Published - Aug 15 2008

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10.1016/j.csda.2008.05.022

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@article{06896370babb43a580e76161e763514b,

title = "Bootstrap quantile estimation via importance resampling",

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.",

author = "Jiaqiao Hu and Zheng Su",

year = "2008",

month = aug,

day = "15",

doi = "10.1016/j.csda.2008.05.022",

language = "English",

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journal = "Computational Statistics and Data Analysis",

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T1 - Bootstrap quantile estimation via importance resampling

AU - Hu, Jiaqiao

AU - Su, Zheng

PY - 2008/8/15

Y1 - 2008/8/15

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/47749130770

U2 - 10.1016/j.csda.2008.05.022

DO - 10.1016/j.csda.2008.05.022

M3 - Article

SN - 0167-9473

VL - 52

SP - 5136

EP - 5142

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 12

ER -

Bootstrap quantile estimation via importance resampling

Abstract

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