The Impact of multiple imputation for DACSEIS

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dc.contributor.author Rässler, Susanne de_DE
dc.contributor.other Münnich, Ralf (Co-ordinator) de_DE
dc.date.accessioned 2004-03-05 de_DE
dc.date.accessioned 2014-03-18T10:01:51Z
dc.date.available 2004-03-05 de_DE
dc.date.available 2014-03-18T10:01:51Z
dc.date.issued 2004 de_DE
dc.identifier.other 110518853 de_DE
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-11352 de_DE
dc.identifier.uri http://hdl.handle.net/10900/47296
dc.description.abstract This paper is designed to provide an extensive introduction to the principles of multiple imputation and to give some general recommendations of using multiple imputation techniques in the DACSEIS universes. The definition of an ignorable missingness mechanism is explained, and the concept of the observed-data likelihood is discussed. To introduce the multiple imputation principle a short introduction of Bayesian statistics is provided. A small simulation study is performed comparing different approaches to illuminate the advantages and disadvantages of different imputation techniques. Finally, an overview about recently available multiple imputation software is given and violations of the assumptions made are addressed. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-nopod de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=en en
dc.subject.classification Imputationstechnik , Stichprobenfehler de_DE
dc.subject.ddc 310 de_DE
dc.subject.other Complexe Stichproben , Monte-Carlo-Simulation , Fehlende Daten de_DE
dc.subject.other Complex survey , Monte-Carlo techniques , missing data en
dc.title The Impact of multiple imputation for DACSEIS en
dc.type WorkingPaper de_DE
utue.publikation.fachbereich Wirtschaftswissenschaften de_DE
utue.publikation.fakultaet 6 Wirtschafts- und Sozialwissenschaftliche Fakultät de_DE
dcterms.DCMIType Text de_DE
utue.publikation.typ workingPaper de_DE
utue.opus.id 1135 de_DE
utue.opus.portal dacseis de_DE
utue.opus.portalzaehlung 0.00000 de_DE

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