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dc.contributor.authorPeng, Yijie
dc.contributor.authorChen, Chun-Hung
dc.contributor.authorFu, Michael
dc.contributor.authorHu, Jian-Qiang
dc.date.accessioned2017-07-03T14:27:39Z
dc.date.available2017-07-03T14:27:39Z
dc.date.issued2017
dc.identifierdoi:10.13016/M2QB9V551
dc.identifier.urihttp://hdl.handle.net/1903/19588
dc.description.abstractThis is the online appendix, which includes theoretical and numerical supplements containing some technical details and three additional numerical examples, which could not fit in the main body due to page limits by the journal for a technical note. The abstract for the main body is as follows: In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy (G-MAP) that takes the induced correlations into account, reflecting a trade-off between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.en_US
dc.description.sponsorshipThis work was supported in part by the National Science Foundation (NSF) under Grants CMMI-0856256, CMMI- 1362303, CMMI-1434419, by the National Natural Science Foundation of China (NSFC) under Grants 71571048, by the Air Force of Scientific Research (AFOSR) under Grant FA9550-15-10050, and by the Science and Technology Agency of Sichuan Province under Grant 2014GZX0002.en_US
dc.subjectsimulation, ranking and selection, Bayesian framework, myopic allocation policy.en_US
dc.titleOnline Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”en_US
dc.typeOtheren_US
dc.relation.isAvailableAtRobert H. Smith School of Businessen_us
dc.relation.isAvailableAtDecision & Information Technologiesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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