标题:Parallel Bootstrap and Optimal Subsample Lengths in Smooth Function Models
作者:Guo, Guangbao; Lin, Lu
作者机构:[Guo, Guangbao; Lin, Lu] Shandong Univ, Sch Math, Jinan 250100, Peoples R China.; [Guo, Guangbao] Shandong Univ Technol, Dept Stat, Zibo, Peoples R 更多
通讯作者:Guo, Guangbao
通讯作者地址:[Guo, GB]Shandong Univ, Sch Math, Jinan 250100, Peoples R China.
来源:COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
出版年:2016
卷:45
期:6
页码:2208-2231
DOI:10.1080/03610918.2014.894767
关键词:Parallel bootstrap; Smooth function models; Subsample length; 60B20;; 62D05; 60H25; 65C10
摘要:Parallel bootstrap is an extremely useful statistical method with good performance. In the present study, we introduce a working correlation matrix on the method, which is called parallel bootstrap matrix. We consider some properties of it and the optimal size of the subsample in smooth function models. We also present some performance results of parallel bootstrap estimators, the subsample length selection on the finance time series data.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975519590&doi=10.1080%2f03610918.2014.894767&partnerID=40&md5=ad6132db171f96b36c9e8facca635a3b
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