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dc.contributor.authorPapalia, Rosa Bernardinien_US
dc.date.accessioned2009-01-29T14:55:04Z-
dc.date.available2009-01-29T14:55:04Z-
dc.date.issued2003en_US
dc.identifier.piurn:nbn:de:kobv:11-10050297-
dc.identifier.urihttp://hdl.handle.net/10419/22244-
dc.description.abstractIn this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME estimates to the parameter and error bounds. Our objective is to show how empirical measures of the value of information for microeconomic models can be estimated in the maximum entropy view.en_US
dc.language.isoengen_US
dc.relation.ispartofseriesDiscussion papers of interdisciplinary research project 373 2003,29en_US
dc.subject.ddc330en_US
dc.subject.keywordGeneralized Maximum Entropyen_US
dc.subject.keywordGeneralized Cross Entropyen_US
dc.subject.keywordRepeated Samplesen_US
dc.subject.keywordMicroeconometric modelsen_US
dc.subject.stwStichprobenverfahrenen_US
dc.subject.stwEntropieen_US
dc.subject.stwMikroökonometrieen_US
dc.subject.stwTheorieen_US
dc.titleModeling the Learning from Repeated Samples: A Generalized Cross Entropy Approachen_US
dc.typeWorking Paperen_US
dc.identifier.ppn379257238en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:sfb373:200329-
Appears in Collections:Discussion Papers, SFB 373, HU Berlin

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