Double-bootstrap methods that use a single double-bootstrap simulation

• 成果类型: Article
• 署名作者: Chang, Jinyuan; Hall, Peter
• 署名单位: Southwestern University of Finance & Economics - China; University of Melbourne
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu060
• 发表日期: 2015
• 页码: 203214
• 关键词: monte-carlo approximation; confidence-intervals; Edgeworth Expansion; iterated bootstrap; error

摘要

We show that, when the double bootstrap is used to improve performance of bootstrap methods for bias correction, techniques based on using a single double-bootstrap sample for each single-bootstrap sample can produce third-order accuracy for much less computational expense than is required by conventional double-bootstrap methods. However, this improved level of performance is not available for the single double-bootstrap methods that have been suggested to construct confidence intervals or distribution estimators.