Please use this identifier to cite or link to this item: https://doi.org/10.1239/aap/1354716593
Title: Rare-event simulation of heavy-tailed random walks by sequential importance sampling and resampling
Authors: Chan, H.P. 
Deng, S.
Lai, T.-L.
Keywords: Efficient simulation
Heavy-tailed distribution
Regularly varying tail
Sequential Monte Carlo
Issue Date: Dec-2012
Citation: Chan, 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
Abstract: We 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.
Source Title: Advances in Applied Probability
URI: http://scholarbank.nus.edu.sg/handle/10635/105323
ISSN: 00018678
DOI: 10.1239/aap/1354716593
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