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Score tests in a generalized linear model with surrogate covariates

dc.contributor.authorSepanski, J. H.en_US
dc.date.accessioned2006-04-10T15:05:17Z
dc.date.available2006-04-10T15:05:17Z
dc.date.issued1992-09-03en_US
dc.identifier.citationSepanski, J. H. (1992/09/03)."Score tests in a generalized linear model with surrogate covariates." Statistics &amp; Probability Letters 15(1): 1-10. <http://hdl.handle.net/2027.42/29852>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6V1D-45DHJ6H-22/2/1a002c214429195d88ff7606ec3241f2en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/29852
dc.description.abstractWe consider generalized linear models where a predictor is measured with error. The efficient score test for the effect of that predictor depends on the regression of the true predictor on its observed surrogate. Using validation data, we estimate the regression by nonparametric techniques. The resulting semiparametric score test is shown to be nearly asymptotically efficient.en_US
dc.format.extent655685 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleScore tests in a generalized linear model with surrogate covariatesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Statistics and Management Science, School of Business Administration, University of Michigan, Ann Arbor, MI, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/29852/1/0000199.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0167-7152(92)90277-Cen_US
dc.identifier.sourceStatistics &amp; Probability Lettersen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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