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dc.contributor.authorKreiss, Jens‐Peteren
dc.contributor.authorPaparoditis Efstathios, E.en
dc.creatorKreiss, Jens‐Peteren
dc.creatorPaparoditis Efstathios, E.en
dc.date.accessioned2019-12-02T10:36:34Z
dc.date.available2019-12-02T10:36:34Z
dc.date.issued2014
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/57187
dc.source.urihttps://nls.ldls.org.uk/welcome.html?ark:/81055/vdc_100025005112.0x000010
dc.subjectStatisticsen
dc.titleBootstrapping locally stationary processesen
dc.typeinfo:eu-repo/semantics/article
dc.description.startingpage1
dc.description.endingpageonline
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>ID: 896en
dc.description.notesIn: Journal of the Royal Statistical Society, Vol. 77, no. 1 (Jan. 2015), p.267-290.en
dc.description.notesSummary: Summary We propose a non‐parametric method to bootstrap locally stationary processes which combines a time domain wild bootstrap approach with a non‐parametric frequency domain approach. The method generates pseudotime series which mimic (asymptotically) correct, the local second‐ and to the necessary extent the fourth‐order moment structure of the underlying process. Thus it can be applied to approximate the distribution of several statistics that are based on observations of the locally stationary process. We prove a bootstrap central limit theorem for a general class of statistics that can be expressed as functionals of the preperiodogram, the latter being a useful tool for inferring properties of locally stationary processes. Some simulations and a real data example shed light on the finite sample properties and illustrate the ability of the bootstrap method proposed.</p>en
dc.contributor.orcidPaparoditis Efstathios, E. [0000-0003-1958-781X]
dc.gnosis.orcid0000-0003-1958-781X


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