Valid resampling of higher-order statistics using the linear process bootstrap and autoregressive sieve bootstrap
Date
2013Author
Jentsch, C.Politis, Dimitris Nicolas
Source
Communications in Statistics - Theory and MethodsVolume
42Issue
7Pages
1277-1293Google Scholar check
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We show that the linear process bootstrap (LPB) and the autoregressive sieve bootstrap (AR sieve) are, in general, not valid for statistics whose large-sample distribution depends on moments of order higher than two, irrespective of whether the data come from a linear time series or not. Inspired by the block-of-blocks bootstrap, we circumvent this non-validity by applying the LPB and AR sieve to suitably blocked data and not to the original data itself. In a simulation study, we compare the LPB, AR sieve, and moving block bootstrap applied directly and to blocked data. Copyright © Taylor & Francis Group, LLC.
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