dc.contributor.author | Jentsch, C. | en |
dc.contributor.author | Politis, Dimitris Nicolas | en |
dc.creator | Jentsch, C. | en |
dc.creator | Politis, Dimitris Nicolas | en |
dc.date.accessioned | 2019-12-02T10:35:44Z | |
dc.date.available | 2019-12-02T10:35:44Z | |
dc.date.issued | 2013 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/56973 | |
dc.description.abstract | 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. | en |
dc.source | Communications in Statistics - Theory and Methods | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877984582&doi=10.1080%2f03610926.2012.698781&partnerID=40&md5=aeeed60f2c154b160d200a317489cbe1 | |
dc.subject | Computer simulation | en |
dc.subject | Time series | en |
dc.subject | Sieves | en |
dc.subject | Resampling | en |
dc.subject | Linear process | en |
dc.subject | AR sieve bootstrap | en |
dc.subject | AR-sieve bootstraps | en |
dc.subject | Autocovariances | en |
dc.subject | Automatic train control | en |
dc.subject | Block bootstrap | en |
dc.subject | Block bootstraps | en |
dc.subject | Generalized means | en |
dc.subject | Higher order statistics | en |
dc.subject | Linear process bootstrap | en |
dc.subject | Resampling of blocks | en |
dc.subject | Sample autocovariances | en |
dc.title | Valid resampling of higher-order statistics using the linear process bootstrap and autoregressive sieve bootstrap | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1080/03610926.2012.698781 | |
dc.description.volume | 42 | |
dc.description.issue | 7 | |
dc.description.startingpage | 1277 | |
dc.description.endingpage | 1293 | |
dc.author.faculty | Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :1</p> | en |
dc.source.abbreviation | Commun Stat Theory Methods | en |