Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/66244 
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dc.contributor.authorGutierrez, Roberto G.en
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2012-10-23-
dc.date.accessioned2012-11-19T15:22:33Z-
dc.date.available2012-11-19T15:22:33Z-
dc.date.issued1995-
dc.identifier.piurn:nbn:de:kobv:11-10063736en
dc.identifier.urihttp://hdl.handle.net/10419/66244-
dc.description.abstractIn parametric regression problems, estimation of the parameter of interest is typically achieved via the solution of a set of unbiased estimating equations. We are interested in problems where in addition to this parameter, the estimating equations consist of an unknown nuisance function which does not depend on the parameter. We study the effects of using a plug-in nonparametric estimator of the nuisance function (for example, a local-linear regression estimator) on the estimability of the parameter. In particular, we specify conditions on the functional estimator which ensure that the parametric rate of consistency for estimating the parameter of interest is preserved, and we give a general asymptotic covariance formula. We apply this theory to three examples.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes |cBerlinen
dc.relation.ispartofseries|aSFB 373 Discussion Paper |x1997,13en
dc.subject.ddc330en
dc.subject.keywordNonparametric Regressionen
dc.subject.keywordMissing Dataen
dc.subject.keywordGeneralized Linear Modelsen
dc.subject.keywordLocal Linear Regressionen
dc.subject.keywordLogistic Regressionen
dc.subject.keywordPartially Linear Modelsen
dc.subject.keywordSemiparametric Regressionen
dc.titlePlug-in semiparametric estimating equations-
dc.typeWorking Paperen
dc.identifier.ppn72832427Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb373:199713en

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