Show simple item record

dc.contributor.authorBiller B.en_US
dc.contributor.authorCorlu, C. G.en_US
dc.date.accessioned2018-04-12T13:45:33Zen_US
dc.date.available2018-04-12T13:45:33Zen_US
dc.date.issued2012en_US
dc.identifier.issn1876-7354en_US
dc.identifier.urihttp://hdl.handle.net/11693/38140en_US
dc.description.abstractIn this survey, we review the copula-based input models that are well suited to provide multivariate input-modeling support for stochastic simulations with dependent inputs. Specifically, we consider the situation in which the dependence between pairs of simulation input random variables is measured by tail dependence (i.e., the amount of dependence in the tails of a bivariate distribution) and review the techniques to construct copula-based input models representing positive tail dependencies. We complement the review with the parameter estimation from multivariate input data and the random-vector generation from the estimated input model with the purpose of driving the simulation. © 2012 Elsevier Ltd.en_US
dc.language.isoEnglishen_US
dc.source.titleSurveys in Operations Research and Management Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.sorms.2012.04.001en_US
dc.titleCopula-based multivariate input modelingen_US
dc.typeReviewen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage69en_US
dc.citation.epage84en_US
dc.citation.volumeNumber17en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1016/j.sorms.2012.04.001en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record