Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80204
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dc.contributorDepartment of Logistics and Maritime Studies-
dc.creatorYe, H-
dc.creatorYao, D-
dc.date.accessioned2019-01-08T03:03:13Z-
dc.date.available2019-01-08T03:03:13Z-
dc.identifier.issn1050-5164en_US
dc.identifier.urihttp://hdl.handle.net/10397/80204-
dc.language.isoenen_US
dc.publisherInstitute of Mathematical Statisticsen_US
dc.rights© Institute of Mathematical Statistics, 2018en_US
dc.rightsThe following publication Ye, H. Q., & Yao, D. D. (2018). Justifying diffusion approximations for multiclass queueing networks under a moment condition. The Annals of Applied Probability, 28(6), 3652-3697, is available at https://doi.org/10.1214/18-AAP1401en_US
dc.rightsPosted with permission of the publisher.en_US
dc.subjectDiffusion limiten_US
dc.subjectInterchange of limitsen_US
dc.subjectMulticlass queueing networken_US
dc.subjectUniform stabilityen_US
dc.titleJustifying diffusion approximations for multiclass queueing networks under a moment conditionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage3652en_US
dc.identifier.epage3697en_US
dc.identifier.volume28en_US
dc.identifier.issue6en_US
dc.identifier.doi10.1214/18-AAP1401en_US
dcterms.abstractMulticlass queueing networks (MQN) are, in general, difficult objects to study analytically. The diffusion approximation refers to using the stationary distribution of the diffusion limit as an approximation of the diffusion-scaled process (say, the workload) in the original MQN. To validate such an approximation amounts to justifying the interchange of two limits, t→∞and k→∞, with t being the time index and k, the scaling parameter. Here, we show this interchange of limits is justified under a p∗th moment condition on the primitive data, the interarrival and service times; and we provide an explicit characterization of the required order (p∗), which depends naturally on the desired order of moment of the workload process.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAnnals of applied probability, 2018, v. 28, no. 6, p. 3652-3697-
dcterms.isPartOfAnnals of applied probability-
dcterms.issued2018-
dc.identifier.isiWOS:000446578800010-
dc.identifier.scopus2-s2.0-85054979786-
dc.identifier.eissn2168-8737en_US
dc.description.validate201901 bcmaen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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