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dc.contributor.authorSchneeweiss, Hansen_US
dc.contributor.authorAugustin, Thomasen_US
dc.date.accessioned2005-10-18en_US
dc.date.accessioned2010-05-14T10:09:23Z-
dc.date.available2010-05-14T10:09:23Z-
dc.date.issued2005en_US
dc.identifier.piurn:nbn:de:bvb:19-epub-1821-5-
dc.identifier.urihttp://hdl.handle.net/10419/31008-
dc.description.abstractA measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field.en_US
dc.language.isoengen_US
dc.publisherTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen Münchenen_US
dc.relation.ispartofseriesDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 452en_US
dc.subject.jelC13en_US
dc.subject.jelC20-
dc.subject.jelC24-
dc.subject.jelC25-
dc.subject.ddc310en_US
dc.subject.keywordMeasurement errorsen_US
dc.subject.keyworderror in variablesen_US
dc.subject.keywordmisclassificationen_US
dc.subject.keywordefficiency comparisonen_US
dc.subject.keywordsurvival analysisen_US
dc.subject.stwRegressionen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwTheorieen_US
dc.titleSome recent advances in measurement error models and methodsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn501294406en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:Discussion papers, SFB 386, LMU München

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