Show simple item record

dc.contributor.authorSengupta, Srijanen
dc.contributor.authorShao, Xiaofengen
dc.contributor.authorWang, Yingchuanen
dc.contributor.authorCavaliere, Giuseppeen
dc.contributor.authorPolitis, Dimitris Nicolasen
dc.contributor.authorRahbek, Andersen
dc.creatorSengupta, Srijanen
dc.creatorShao, Xiaofengen
dc.creatorWang, Yingchuanen
dc.creatorCavaliere, Giuseppeen
dc.creatorPolitis, Dimitris Nicolasen
dc.creatorRahbek, Andersen
dc.titleThe Dependent Random Weightingen
dc.description.endingpageonlineΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.description.notes<p>ID: 993en
dc.description.notesIn: JOURNAL OF TIME SERIES ANALYSIS volume 36 issue 3 page 315.en
dc.description.notesSummary: Abstract We propose a new resampling method, the dependent random weighting, for both time series and random fields. The method is a generalization of the traditional random weighting in that the weights are made to be temporally or spatially dependent and are adaptive to the configuration of the data. Unlike the block‐based bootstrap or subsampling methods, the dependent random weighting can be used for irregularly spaced time series and spatial data without any implementational difficulty. Consistency of the distribution approximation is shown for both equally and unequally spaced time series. Simulation studies illustrate the finite sample performance of the dependent random weighting in comparison with the existing counterparts for both one‐dimensional and two‐dimensional irregularly spaced data..</p>en
dc.contributor.orcidCavaliere, Giuseppe [0000-0002-2856-0005]

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record