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Rare-Event Simulation of Heavy-Tailed Random Walks by Sequential Importance Sampling and Resampling
| Content Provider | Scilit |
|---|---|
| Author | Chan, Hock Peng Deng, Shaojie Lai, Tze-Leung |
| Copyright Year | 2012 |
| Description | 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. |
| Related Links | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/493DF0CE27F9A5A5DEE3038E8A49EE6D/S000186780000608Xa.pdf/div-class-title-rare-event-simulation-of-heavy-tailed-random-walks-by-sequential-importance-sampling-and-resampling-div.pdf |
| Ending Page | 1196 |
| Page Count | 24 |
| Starting Page | 1173 |
| ISSN | 00018678 |
| e-ISSN | 14756064 |
| DOI | 10.1017/s000186780000608x |
| Journal | Advances in Applied Probability |
| Issue Number | 04 |
| Volume Number | 44 |
| Language | English |
| Publisher | Cambridge University Press (CUP) |
| Publisher Date | 2012-12-01 |
| Access Restriction | Open |
| Subject Keyword | Advances in Applied Probability Statistics and Probability Efficient Simulation tailed Distribution Sequential Monte Carlo Regularly Varying Tail |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Statistics and Probability |
