Full metadata record
DC FieldValueLanguage
dc.contributor.authorFunke, Benedikt-
dc.contributor.authorKawka, Rafael-
dc.date.accessioned2014-12-03T16:51:45Z-
dc.date.available2014-12-03T16:51:45Z-
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/2003/33762-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-84-
dc.description.abstractIn this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric multivariate density estimation. We deal with positively supported data but our results can easily be ex- tended to the case of mixtures of bounded and unbounded supports. Both methods improve the optimal rate of convergence of the mean squared error up to O(n-8=(8+d)), where d is the dimension of the under- lying data set. Moreover, they overcome the boundary effect near the origin and their values are always non-negative. We investigate asymptotic properties like bias and variance as well as the performance of our estimators in Monte Carlo Simulations and in a real data example.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;39/2014-
dc.subjectasymmetric kernelsen
dc.subjectmultivariate density estimationen
dc.subjectbias correctionen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleNonparametric density estimation for multivariate bounded data using two nonnegative multiplicative bias correction methodsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_3914_SFB823_Funke_Kawka.pdfDNB966.76 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org