Bayesian demand updating in lost sales newsvendor: a two moment approximation

dc.citation.epage281en_US
dc.citation.issueNumber1en_US
dc.citation.spage256en_US
dc.citation.volumeNumber182en_US
dc.contributor.authorBerk, E.en_US
dc.contributor.authorGürler, Ü.en_US
dc.contributor.authorLevine, R. A.en_US
dc.date.accessioned2015-07-28T11:58:09Z
dc.date.available2015-07-28T11:58:09Z
dc.date.issued2007-10en_US
dc.departmentDepartment of Managementen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractWe consider Bayesian updating of demand in a lost sales newsvendor model with censored observations. In a lost sales environment, where the arrival process is not recorded, the exact demand is not observed if it exceeds the beginning stock level, resulting in censored observations. Adopting a Bayesian approach for updating the demand distribution, we develop expressions for the exact posteriors starting with conjugate priors, for negative binomial, gamma, Poisson and normal distributions. Having shown that non-informative priors result in degenerate predictive densities except for negative binomial demand, we propose an approximation within the conjugate family by matching the first two moments of the posterior distribution. The conjugacy property of the priors also ensure analytical tractability and ease of computation in successive updates. In our numerical study, we show that the posteriors and the predictive demand distributions obtained exactly and with the approximation are very close to each other, and that the approximation works very well from both probabilistic and operational perspectives in a sequential updating setting as well.en_US
dc.identifier.doi10.1016/j.ejor.2006.08.035en_US
dc.identifier.eissn1872-6860
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/11693/11596
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.ejor.2006.08.035en_US
dc.source.titleEuropean Journal of Operational Researchen_US
dc.subjectInventoryen_US
dc.subjectNewsboyen_US
dc.subjectLost Salesen_US
dc.subjectCensoringen_US
dc.subjectBayesianen_US
dc.titleBayesian demand updating in lost sales newsvendor: a two moment approximationen_US
dc.typeArticleen_US
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