Please use this identifier to cite or link to this item: https://doi.org/10.1239/aap/1354716593
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dc.titleRare-event simulation of heavy-tailed random walks by sequential importance sampling and resampling
dc.contributor.authorChan, H.P.
dc.contributor.authorDeng, S.
dc.contributor.authorLai, T.-L.
dc.date.accessioned2014-10-28T05:14:37Z
dc.date.available2014-10-28T05:14:37Z
dc.date.issued2012-12
dc.identifier.citationChan, H.P., Deng, S., Lai, T.-L. (2012-12). Rare-event simulation of heavy-tailed random walks by sequential importance sampling and resampling. Advances in Applied Probability 44 (4) : 1173-1196. ScholarBank@NUS Repository. https://doi.org/10.1239/aap/1354716593
dc.identifier.issn00018678
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105323
dc.description.abstractWe introduce a new approach to simulating rare events for Markov random walks with heavy-tailed increments. This approach involves sequential importance sampling and resampling, and uses a martingale representation of the corresponding estimate of the rare-event probability to show that it is unbiased and to bound its variance. By choosing the importance measures and resampling weights suitably, it is shown how this approach can yield asymptotically efficient Monte Carlo estimates. © Applied Probability Trust 2012.
dc.sourceScopus
dc.subjectEfficient simulation
dc.subjectHeavy-tailed distribution
dc.subjectRegularly varying tail
dc.subjectSequential Monte Carlo
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1239/aap/1354716593
dc.description.sourcetitleAdvances in Applied Probability
dc.description.volume44
dc.description.issue4
dc.description.page1173-1196
dc.description.codenAAPBB
dc.identifier.isiutNOT_IN_WOS
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