ON GENERAL RESAMPLING ALGORITHMS AND THEIR PERFORMANCE IN DISTRIBUTION ESTIMATION

• 成果类型: Article
• 署名作者: HALL, P; MAMMEN, E
• 署名单位: Ruprecht Karls University Heidelberg
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325769
• 发表日期: 1994
• 页码: 2011-2030
• 关键词: bayesian bootstrap; regression-analysis; jackknife; statistics

摘要

Recent work of several authors has focussed on first-order properties (e.g, consistency) of general bootstrap algorithms, where the numbers of times that data values are resampled form an exchangeable sequence. In the present paper we develop second-order properties of such algorithms, in a very general setting. Performance is discussed in the context of distribution estimation, and formulae for higher-order moments and cumulants are developed. Arguing thus, necessary and sufficient conditions are given for general resampling algorithms to correctly capture second-order properties.