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dc.contributor.authorStøve, Bård
dc.contributor.authorTjøstheim, Dag
dc.date.accessioned2008-01-14T12:04:21Z
dc.date.available2008-01-14T12:04:21Z
dc.date.issued2007-11
dc.identifier.issn1500-4066
dc.identifier.urihttp://hdl.handle.net/11250/163901
dc.description.abstractThe problem of estimating an unknown density function has been widely studied. In this paper we present a convolution estimator for the density of the responses in a nonlinear regression model. The rate of convergence for the variance of the convolution estimator is of order n-1. This is faster than the rate for the kernel density method. The intuition behind this result is that the convolution estimator uses model information, and thus an improvement can be expected. We also derive the bias of the new estimator and conduct simulation experiments to check the finite sample properties. The proposed estimator performs substantially better than the kernel density estimator for well-behaved noise densities.en
dc.language.isoengen
dc.publisherNorwegian School of Economics and Business Administration. Department of Finance and Management Scienceen
dc.relation.ispartofseriesDiscussion paperen
dc.relation.ispartofseries2007:25en
dc.subjectconvergence rateen
dc.subjectconvolution estimatoren
dc.subjectkernel functionen
dc.subjectmean squared erroren
dc.subjectnonparametric density estimationen
dc.titleA convolution estimator for the density of nonlinear regression observationsen
dc.typeWorking paperen
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Bedriftsøkonomi: 213en


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