Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31896
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dc.contributor.authorKiesl, Hansen_US
dc.contributor.authorRässler, Susanneen_US
dc.date.accessioned2006-10-10en_US
dc.date.accessioned2010-05-14T11:22:40Z-
dc.date.available2010-05-14T11:22:40Z-
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10419/31896-
dc.description.abstractData fusion techniques typically aim to achieve a complete data file from different sources which do not contain the same units. Traditionally, this is done on the basis of variables common to all files. It is well known that those approaches establish conditional independence of the specific variables given the common variables, although they may be conditionally dependent in reality. We discuss the objectives of data fusion in the light of their feasibility and distinguish four levels of validity that a fusion technique may achieve. For a rather general situation, we derive the feasible set of correlation matrices for the variables not jointly observed and suggest a new quality index for data fusion. Finally, we present a suitable and effcient multiple imputation procedure to make use of auxiliary information and to overcome the conditional independence assumption.en_US
dc.language.isoengen_US
dc.publisher|aIAB|cNürnbergen_US
dc.relation.ispartofseries|aIAB discussion paper|x2006,15en_US
dc.subject.jelC11en_US
dc.subject.jelC15en_US
dc.subject.jelC81en_US
dc.subject.ddc330en_US
dc.subject.keywordDatenen_US
dc.subject.keywordDatenaufbereitungen_US
dc.subject.keywordDatenqualitäten_US
dc.subject.keywordKorrelationen_US
dc.subject.keywordValiditäten_US
dc.subject.keywordangewandte Statistiken_US
dc.subject.keywordmathematische Statistiken_US
dc.titleHow valid can data fusion be?en_US
dc.type|aWorking Paperen_US
dc.identifier.ppn518523101en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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