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dc.contributor.authorDette, Holger-
dc.contributor.authorScheder, Regine-
dc.date.accessioned2005-07-07T12:28:29Z-
dc.date.available2005-07-07T12:28:29Z-
dc.date.issued2005-07-07T12:28:29Z-
dc.identifier.urihttp://hdl.handle.net/2003/21514-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6670-
dc.description.abstractIn this article a new monotone nonparametric estimate for a regression function of two or more variables is proposed. The method starts with an unconstrained nonparametric regression estimate and uses successively one-dimensional isotonization procedures. In the case of a strictly monotone regression function, it is shown that the new estimate is first order asymptotic equivalent to the unconstrained estimate, and asymptotic normality of an appropriate standardization of the estimate is established. Moreover, if the regression function is not monotone in one of its arguments, the constructed estimate has approximately the same Lp-norm as the initial unconstrained estimate. The methodology is also illustrated by means of a simulation study, and two data examples are analyzed. AMS Subject Classification: 62G05, 62G20en
dc.format.extent1068611 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.subjectIsotonic regressionen
dc.subjectMultivariate nonparametric regressionen
dc.subjectNondecreasing rearrangementen
dc.subjectOrder restricted inferenceen
dc.subject.ddc004-
dc.titleStrictly monotone and smooth nonparametric regression for two or more variablesen
dc.typeTexten
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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