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High order data sharpening for density estimation

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dc.contributor.authorHall, Peter
dc.contributor.authorMinnotte, M
dc.date.accessioned2015-12-13T22:23:17Z
dc.date.available2015-12-13T22:23:17Z
dc.date.issued2002
dc.date.updated2015-12-11T08:04:20Z
dc.description.abstractIt is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily high orders of bias reduction. Practical advantages of this approach, relative to competing methods, are demonstrated. They include the sheer simplicity of the estimators, which makes code for computing them particularly easy to write, very good mean-squared error performance, reduced 'wiggliness' of estimates and greater robustness against undersmoothing.
dc.identifier.issn1369-7412
dc.identifier.urihttp://hdl.handle.net/1885/72701
dc.publisherAiden Press
dc.sourceJournal of the Royal Statistical Society Series B
dc.subjectKeywords: Bandwidth; Bias reduction; Kernel methods; Local polynomial methods; Mean-squared error; Nonparametric curve estimation; Transformation methods; Wiggliness
dc.titleHigh order data sharpening for density estimation
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage157
local.bibliographicCitation.startpage141
local.contributor.affiliationHall, Peter, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationMinnotte, M, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidHall, Peter, u7801145
local.contributor.authoruidMinnotte, M, t456
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub3386
local.identifier.citationvolume64
local.identifier.doi10.1111/1467-9868.00329
local.identifier.scopusID2-s2.0-0036003422
local.type.statusPublished Version

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