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dc.contributor.authorAndersson, Jonas
dc.contributor.authorJörnsten, Kurt
dc.contributor.authorNonås, Sigrid Lise
dc.contributor.authorSandal, Leif Kristoffer
dc.contributor.authorUbøe, Jan
dc.date.accessioned2013-03-08T09:27:11Z
dc.date.available2013-03-08T09:27:11Z
dc.date.issued2011-08
dc.identifier.urihttp://hdl.handle.net/11250/164175
dc.description.abstractIn this paper, we consider the newsvendor model under partial information, i.e., where the demand distribution D is partly unknown. We focus on the classical case where the retailer only knows the expectation and variance of D. The standard approach is then to determine the order quantity using conservative rules such as minimax regret or Scarf's rule. We compute instead the most likely demand distribution in the sense of maximum entropy. We then compare the performance of the maximum entropy approach with minimax regret and Scarf's rule on large samples of randomly drawn demand distributions. We show that the average performance of the maximum entropy approach is considerably better than either alternative, and more surprisingly, that it is in most cases a better hedge against bad results.no_NO
dc.language.isoengno_NO
dc.publisherNorwegian School of Economics. Department of Finance and Management Scienceno_NO
dc.relation.ispartofseriesDiscussion paper;2011:14
dc.subjectnewsvendor modelno_NO
dc.subjectentropyno_NO
dc.subjectpartial informationno_NO
dc.titleA maximum entropy approach to the newsvendor problem with partial informationno_NO
dc.typeWorking paperno_NO
dc.subject.nsiVDP::Social science: 200::Economics: 210::Business: 213no_NO


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