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2006-05-19Buch DOI: 10.18452/3799
Estimating Covariance Matrices Using Estimating Functions in Nonparametric and Semiparametric Regression
dc.contributor.authorCarroll, Raymond J.
dc.contributor.authorIturria, Stephen J.
dc.contributor.authorGutierrez, Roberto G.
dc.date.accessioned2017-06-15T22:09:32Z
dc.date.available2017-06-15T22:09:32Z
dc.date.created2006-05-19
dc.date.issued2006-05-19
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4451
dc.description.abstractWe use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric regressions.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectNonparametric regressioneng
dc.subjectEstimating Equationseng
dc.subjectKernel regressioneng
dc.subjectPlug-in Semiparametricseng
dc.subjectSmoothingeng
dc.subject.ddc330 Wirtschaft
dc.titleEstimating Covariance Matrices Using Estimating Functions in Nonparametric and Semiparametric Regression
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10063741
dc.identifier.doihttp://dx.doi.org/10.18452/3799
dc.subject.dnb17 Wirtschaft
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages6
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume1997
local.edoc.container-issue14
local.edoc.container-year1997
local.edoc.container-erstkatid2135319-0

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