Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/22563
DC FieldValueLanguage
dc.contributor.authorDeo, Anand
dc.contributor.authorMurthy, Karthyek
dc.date.accessioned2024-02-20T05:58:43Z-
dc.date.available2024-02-20T05:58:43Z-
dc.date.issued2023
dc.identifier.issn0030-364X
dc.identifier.issn1526-5463
dc.identifier.urihttps://repository.iimb.ac.in/handle/2074/22563-
dc.description.abstractThis paper presents a novel importance sampling (IS) scheme for estimating distribution tails of performance measures modeled with a rich set of tools, such as linear programs, integer linear programs, piecewise linear/quadratic objectives, feature maps specified with deep neural networks, etc. The conventional approach of explicitly identifying efficient changes of measure suffers from feasibility and scalability concerns beyond highly stylized models because of their need to be tailored intricately to the objective and the underlying probability distribution. This bottleneck is overcome in the proposed scheme with an elementary transformation that is capable of implicitly inducing an effective IS distribution in a variety of models by replicating the concentration properties observed in less rare samples. This novel approach is guided by developing a large deviations principle that brings out the phenomenon of self-similarity of optimal IS distributions. The proposed sampler is the first to attain asymptotically optimal variance reduction across a spectrum of multivariate distributions despite being oblivious to the specifics of the underlying model. Its applicability is illustrated with contextual shortest-path and portfolio credit risk models informed by neural networks.
dc.publisherINFORMS
dc.subjectVariance reduction
dc.subjectTail risks
dc.subjectRare event simulation
dc.subjectImportance sampling
dc.subjectContextual models
dc.subjectPortfolio credit risk
dc.subjectValue-at-risk
dc.subjectConditional value-at-risk
dc.subjectLog efficient
dc.titleAchieving Efficiency in Black-Box Simulation of Distribution Tails with Self-Structuring Importance Samplers
dc.typeJournal Article
dc.identifier.doi10.1287/opre.2021.0331
dc.journal.nameOperations Research
Appears in Collections:2020-2029 C
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.