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dc.contributor.authorGilberg, Frankde
dc.contributor.authorUrfer, Wolfgangde
dc.date.accessioned2004-12-06T18:38:57Z-
dc.date.available2004-12-06T18:38:57Z-
dc.date.issued1998de
dc.identifier.urihttp://hdl.handle.net/2003/4880-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5320-
dc.description.abstractWe discuss an extansion of the nonlinear random effects model from Lindstrom and Bates (1990) by adding a flexible transformation to both sides of the model (see Carroll and Ruppert (1988)) and describe a procedure for parameter estimation. This method combines pseudo maximum likelihood estimators for the transform-both-sides and weighting model and maximum likelihood (or restricted maximum likelihood) estimatiors for the linear mixed effects models. A validation of this new method is performed by analyzing a simulated set of enzyme kinetic data published by Jones (1993).en
dc.format.extent1456179 bytes-
dc.format.extent1784706 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectenzyme kineticsen
dc.subjectnonlinear regressionen
dc.subjectPseudo likelihood estimationen
dc.subjectrandom effectsen
dc.subjectrepeated measures dataen
dc.subjecttransformation and weightingen
dc.subject.ddc310de
dc.titleHeteroscedastic nonlinear regression models with random effectsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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