文章基本信息

标题：Properties of a Block Bootstrap under Long-range Dependence
本地全文：下载
作者：Young Min Kim ; Daniel J. Nordman Iowa State University, Ames, USA
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2011
卷号：73
期号：01
页码：79--109
出版社：Indian Statistical Institute
摘要：The block bootstrap has been largely developed for weakly dependent time processes and, in this context, much research has focused on the large-sample properties of block bootstrap inference about sample means. This work validates the block bootstrap for distribution estimation with stationary, linear processes exhibiting strong dependence. For estimating the sample mean's variance under long-memory, explicit expressions are also provided for the bias and variance of moving and non-overlapping block bootstrap estimators. These di er critically from the weak dependence setting and optimal blocks decrease in size as the strong dependence increases. The ndings in distribution and variance estimation are then illustrated using simulation.
关键词：Block size, condence interval, sample average, variance estimation.