dc.contributor.author | Politis, Dimitris Nicolas | en |
dc.creator | Politis, Dimitris Nicolas | en |
dc.date.accessioned | 2019-12-02T10:37:52Z | |
dc.date.available | 2019-12-02T10:37:52Z | |
dc.date.issued | 2003 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/57517 | |
dc.description.abstract | Sparked by Efron's seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data - mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric inference procedures based on resampling and/or subsampling has freed practitioners from the necessity of resorting to simplifying assumptions such as normality or linearity that may be misleading. | en |
dc.source | Statistical Science | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0346955658&doi=10.1214%2fss%2f1063994977&partnerID=40&md5=413dce4dcca4ea05679f535d79b3579a | |
dc.subject | Nonparametric estimation | en |
dc.subject | Confidence intervals | en |
dc.subject | Resampling | en |
dc.subject | Subsampling | en |
dc.subject | Large sample inference | en |
dc.subject | Block bootstrap | en |
dc.subject | Linear models | en |
dc.title | The Impact of Bootstrap Methods on Time Series Analysis | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1214/ss/1063994977 | |
dc.description.volume | 18 | |
dc.description.issue | 2 | |
dc.description.startingpage | 219 | |
dc.description.endingpage | 230 | |
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 :142</p> | en |
dc.source.abbreviation | Stat.Sci. | en |