Consistency of the jackknife-after-bootstrap variance estimator for the bootstrap quantiles of a studentized statistic

• 成果类型: Article
• 署名作者: Lahiri, SN
• 署名单位: Iowa State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053605000000507
• 发表日期: 2005
• 页码: 2475-2506
• 关键词: dependent random vectors; stationary observations; asymptotic expansions; Edgeworth Expansion; blockwise bootstrap; random-variables; ORDER; sums; validity

摘要

Efron [J Roy. Statist. Soc. Ser. B 54 (1992) 83-111] proposed a computationally efficient method, called the jackknife-after-bootstrap, for estimating the variance of a bootstrap estimator for independent data. For dependent data, a version of the jackk-iiife-after-bootstrap method has been recently proposed by Lahiri [Econometric Theory 18 (2002) 79-98.]. In this paper it is shown that the jackknife-after-bootstrap estimators of the variance of a bootstrap quantile are consistent for both dependent and independent data. Results from a simulation study are also presented.