On a class of m out of n bootstrap confidence intervals

• 成果类型: Article
• 署名作者: Lee, SMS
• 署名单位: University of Hong Kong
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00209
• 发表日期: 1999
• 页码: 901-911
• 关键词: error

摘要

It is widely known that bootstrap failure can often be remedied by using a technique known as the 'm out of n' bootstrap, by which a smaller number, m say, of observations are resampled from the original sample of size n, In successful cases of the bootstrap, the m out of n bootstrap is often deemed unnecessary. We show that the problem of constructing nonparametric confidence intervals is an exceptional case. By considering a new class of m out of n bootstrap confidence limits, we develop a computationally efficient approach based on the double bootstrap to construct the optimal m out of n bootstrap intervals. We show that the optimal intervals have a coverage accuracy which is comparable with that of the classical double-bootstrap intervals, and we conduct a simulation study to examine their performance. The results are in general very encouraging. Alternative approaches which yield even higher order accuracy are also discussed.