Asymptotically efficient estimation of the sparsity function at a point

dc.contributor.authorWelsh, A. H.en
dc.date.accessioned2026-01-01T10:41:24Z
dc.date.available2026-01-01T10:41:24Z
dc.date.issued1988en
dc.description.abstractThe sparsity function is important in nonparametric inference based on order statistics. In this paper, we consider kernel estimation of the sparsity function. We establish an invariance principle for the kernel estimator and then construct a simple adaptive estimator which we show is asymptotically efficient in the mean squared error sense.en
dc.description.sponsorshipSupport for this research was provided in part by National Science Foundation Grant No.en
dc.description.statusPeer-revieweden
dc.format.extent6en
dc.identifier.issn0167-7152en
dc.identifier.scopus38249031035en
dc.identifier.urihttps://hdl.handle.net/1885/733799707
dc.language.isoenen
dc.sourceStatistics and Probability Lettersen
dc.subjectefficient estimationen
dc.subjectkernelen
dc.subjectmean squared erroren
dc.subjectorder statisticsen
dc.subjectsparsity functionen
dc.subjectweak convergenceen
dc.titleAsymptotically efficient estimation of the sparsity function at a pointen
dc.typeJournal articleen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage432en
local.bibliographicCitation.startpage427en
local.contributor.affiliationWelsh, A. H.; Department of Statisticsen
local.identifier.citationvolume6en
local.identifier.doi10.1016/0167-7152(88)90103-4en
local.identifier.pure0896ab1e-3c5a-468e-9838-3df0ce92088aen
local.identifier.urlhttps://www.scopus.com/pages/publications/38249031035en
local.type.statusPublisheden

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