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dc.contributor.authorGyörfi, László
dc.contributor.authorWalk, Harro
dc.date.accessioned2015-05-18T12:00:00Z
dc.date.accessioned2016-10-05T14:14:23Z
dc.date.available2015-05-18T12:00:00Z
dc.date.available2016-10-05T14:14:23Z
dc.date.issued2012
dc.identifier.urihttp://publications.mfo.de/handle/mfo/1210
dc.descriptionResearch in Pairs 2012en_US
dc.description.abstractConsider the regression problem with a response variable $Y$ and with a $d$-dimensional feature vector $X$. For the regression function $m(x) = \mathbb{E}\{Y|X = x\}$, this paper investigates methods for estimating the density of the residual $Y -m(X)$ from independent and identically distributed data. For heteroscedastic regression, we prove the strong universal (density-free) $L_1$-consistency of a recursive and a nonrecursive kernel density estimate based on a regression estimate.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesOberwolfach Preprints;2012,07
dc.subjectRegression residualen_US
dc.subjectNonparametric kernel density estimationen_US
dc.subjectNonparametric regression estimationen_US
dc.subjectHeteroscedastic regressionen_US
dc.titleStrongly Consistent Density Estimation of Regression Redidualen_US
dc.typePreprinten_US
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWP-2012-07
local.scientificprogramResearch in Pairs 2012
local.series.idOWP-2012-07
local.subject.msc62
dc.identifier.urnurn:nbn:de:101:1-201207047025
dc.identifier.ppn1651531307


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