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dc.contributor.authorKüchenhoff, Helmuten_US
dc.contributor.authorLederer, Wolfgangen_US
dc.contributor.authorLesaffre, Emmanuelen_US
dc.date.accessioned2006-04-25en_US
dc.date.accessioned2010-05-14T10:10:31Z-
dc.date.available2010-05-14T10:10:31Z-
dc.date.issued2006en_US
dc.identifier.piurn:nbn:de:bvb:19-epub-1841-6-
dc.identifier.urihttp://hdl.handle.net/10419/31104-
dc.description.abstractMost epidemiological studies suffer from misclassification in the response and/or the covariates. Since ignoring misclassification induces bias on the parameter estimates, correction for such errors is important. For measurement error, the continuous analog to misclassification, a general approach for bias correction is the SIMEX (simulation extrapolation) originally suggested by Cook and Stefanski (1994). This approach has been recently extended to regression models with a possibly misclassified categorical response and/or the covariates by K¨uchenhoff et al. (2005), and is called the MC-SIMEX approach. To assess the importance of a regressor not only its (corrected) estimate is needed, but also its standard error. For the original SIMEX approach. Carroll et al. (1996) developed a method for estimating the asymptotic variance. Here we derive the asymptotic variance estimators for the MC-SIMEX approach, extending the methodology of Carroll et al. (1996). We also include the case where the misclassification probabilities are estimated by a validation study. An extensive simulation study shows the good performance of our approach. The approach is illustrated using an example in caries research including a logistic regression model, where the response and a binary covariate are possibly misclassified.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 473en_US
dc.subject.ddc310en_US
dc.subject.keywordmisclassificationen_US
dc.subject.keywordSIMEX approachen_US
dc.subject.keywordvariance estimationen_US
dc.titleAsymptotic Variance Estimation for the Misclassification SIMEXen_US
dc.typeWorking Paperen_US
dc.identifier.ppn510830471en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:Discussion papers, SFB 386, LMU München

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