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The generalised bootstrap for clustered data

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dc.contributor.authorPang, Zhen
dc.contributor.authorWelsh, Alan
dc.date.accessioned2015-12-10T22:39:27Z
dc.date.available2015-12-10T22:39:27Z
dc.date.issued2014
dc.date.updated2015-12-09T10:51:22Z
dc.description.abstractWe extend the generalised bootstrap of Chatterjee and Bose (2005) to bootstrap clustered data. We show by simulations and theoretical arguments that the variance of the random weights used in the generalised bootstrap is critical in determining the performance of the bootstrap when we use the distribution of the bootstrap estimate to approximate the sampling distribution of the parameter. In particular, we show that for consistency, the weights should be chosen to have variance one.
dc.identifier.issn1755-8050
dc.identifier.urihttp://hdl.handle.net/1885/57178
dc.publisherInderscience Publishers
dc.sourceInternational Journal of Data Analysis Techniques and Strategies
dc.titleThe generalised bootstrap for clustered data
dc.typeJournal article
local.bibliographicCitation.issue4
local.bibliographicCitation.lastpage415
local.bibliographicCitation.startpage407
local.contributor.affiliationPang, Zhen, Nanyang Technological University
local.contributor.affiliationWelsh, Alan, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidWelsh, Alan, u8204947
local.description.notesImported from ARIES
local.identifier.absfor010400 - STATISTICS
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
local.identifier.ariespublicationa383154xPUB390
local.identifier.citationvolume6
local.identifier.doi10.1504/IJDATS.2014.066604
local.identifier.scopusID2-s2.0-84920163730
local.type.statusPublished Version

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