dc.contributor.author Andersson, Jonas dc.contributor.author Jörnsten, Kurt dc.contributor.author Nonås, Sigrid Lise dc.contributor.author Sandal, Leif Kristoffer dc.contributor.author Ubøe, Jan dc.date.accessioned 2013-03-08T09:27:11Z dc.date.available 2013-03-08T09:27:11Z dc.date.issued 2011-08 dc.identifier.uri http://hdl.handle.net/11250/164175 dc.description.abstract In this paper, we consider the newsvendor model under partial information, i.e., where no_NO 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. dc.language.iso eng no_NO dc.publisher Norwegian School of Economics. Department of Finance and Management Science no_NO dc.relation.ispartofseries Discussion paper;2011:14 dc.subject newsvendor model no_NO dc.subject entropy no_NO dc.subject partial information no_NO dc.title A maximum entropy approach to the newsvendor problem with partial information no_NO dc.type Working paper no_NO dc.subject.nsi VDP::Social science: 200::Economics: 210::Business: 213 no_NO
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