A comparative study of monotone nonparametric kernel estimates

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dc.contributor.authorDette, Holgerde
dc.contributor.authorPilz, Kay F.de
dc.date.accessioned2004-12-06T18:39:09Z
dc.date.available2004-12-06T18:39:09Z
dc.date.issued2004de
dc.format.extent198424 bytes
dc.format.extent301258 bytes
dc.format.extent448842 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/4892
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6924
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectIn this paper we present a detailed numerical comparison of three monotone nonparametric kernel regression estimates, which isotonize a nonparametric curve estimator. The first estimate is the classical smoothed isotone estimate of Brunk (1958). The second method has recently been proposed by Hall and Huang (2001) and modifies the weights of a commonly used kernel estimate such that the resulting estimate is monotone. The third estimate was recently proposed by Dette, Neumeyer and Pilz (2003) and combines density and regression estimation techniques to obtain a monotone curve estimate of the inverse of the isotone regression function. The three concepts are briefly reviewed and their finite sample properties are studied by means of a simulation study. Although all estimates are first order asymptotically equivalent (provided that the unknown regression function is isotone) some differences for moderate samples are observed.en
dc.subjectisotonic regressionen
dc.subjectorder restricted inferenceen
dc.subjectNadaraya-Watson estimatoren
dc.subjectlocal linear regressionen
dc.subjectmonte carlo simulationen
dc.subject.ddc310de
dc.titleA comparative study of monotone nonparametric kernel estimatesen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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