Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
417205 | Computational Statistics & Data Analysis | 2008 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Jiaqiao Hu, Zheng Su,