Please use this identifier to cite or link to this item: https://doi.org/10.1214/105051606000000664
Title: Efficient importance sampling for monte carlo evaluation of exceedance probabilities
Authors: Chan, H.P. 
Lai, T.L.
Keywords: Boundary crossing probability
Importance sampling
Markov additive process
Regeneration
Issue Date: Apr-2007
Citation: Chan, H.P., Lai, T.L. (2007-04). Efficient importance sampling for monte carlo evaluation of exceedance probabilities. Annals of Applied Probability 17 (2) : 440-473. ScholarBank@NUS Repository. https://doi.org/10.1214/105051606000000664
Abstract: Large deviation theory has provided important clues for the choice of importance sampling measures for Monte Carlo evaluation of exceedance probabilities. However, Glasserman and Wang [Ann. Appl. Probab. 7 (1997) 731-746] have given examples in which importance sampling measures that are consistent with large deviations can perform much worse than direct Monte Carlo. We address this problem by using certain mixtures of exponentially twisted measures for importance sampling. Their asymptotic optimality is established by using a new class of likelihood ratio martingales and renewal theory. © Institute of Mathematical Statistics, 2007.
Source Title: Annals of Applied Probability
URI: http://scholarbank.nus.edu.sg/handle/10635/105111
ISSN: 10505164
DOI: 10.1214/105051606000000664
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