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2011-03-14Buch DOI: 10.18452/4306
Oracally Efficient Two-Step Estimation of Generalized Additive Model
dc.contributor.authorLiu, Rong
dc.contributor.authorYang, Lijian
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2017-06-16T00:19:11Z
dc.date.available2017-06-16T00:19:11Z
dc.date.created2011-04-19
dc.date.issued2011-03-14
dc.date.submitted2011-03-14
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4958
dc.description.abstractGeneralized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency. The SBK techniques is both computational expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates with the asymptotic theory.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectmixingeng
dc.subjectBandwidthseng
dc.subjectB splineeng
dc.subjectknotseng
dc.subjectlink functioneng
dc.subjectNadaraya-Watson estimatoreng
dc.subject.ddc330 Wirtschaft
dc.titleOracally Efficient Two-Step Estimation of Generalized Additive Model
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100185844
dc.identifier.doihttp://dx.doi.org/10.18452/4306
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages44
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2011
local.edoc.container-issue16
local.edoc.container-year2011
local.edoc.container-erstkatid2195055-6

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