Optimal Bootstrap Sample Size in Construction of Percentile Confidence Bounds

Kam‐Hin Chung and Stephen M. S. Lee

Scandinavian Journal of Statistics, 2001, vol. 28, issue 1, 225-239

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.

Date: 2001
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