A General Kernel Functional Estimator with Generalized Bandwidth - Strong Consistency and Applications

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dc.contributor.authorWeißbach, Rafaelde
dc.date.accessioned2004-12-06T18:39:10Z
dc.date.available2004-12-06T18:39:10Z
dc.date.created2004de
dc.date.issued2004-08-17de
dc.description.abstractWe consider the problem of uniform asymptotics in kernel functional estimation where the bandwidth can depend on the data. In a unified approach we investigate kernel estimates of the density and the hazard rate for uncensored and right-censored observations. The model allows for the fixed bandwidth as well as for various variable bandwidths, e.g. the nearest neighbor bandwidth. An elementary proof for the strong consistency of the generalized estimator is given that builds on the local convergence of the empirical process against the cumulative distribution function and the Nelson-Aalen estimator against the cumulative hazard rate, respectively.en
dc.format.extent146645 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/4893
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6929
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectfunctional estimationen
dc.subjectdensityen
dc.subjecthazard rateen
dc.subjectkernel smoothingen
dc.subjectuniform consistencyen
dc.subjectempirical processen
dc.subjectnearest neighbor bandwidthen
dc.subjectrandom censorshipen
dc.subject.ddc310de
dc.titleA General Kernel Functional Estimator with Generalized Bandwidth - Strong Consistency and Applicationsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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