J. H. Blanchet and J. Liu, “Efficient Simulation for Large Deviation Probabilities of Sums of Heavy-Tailed Increments,” Proceedings of the 2006 Winter Simulation Conference, Monterey, CA, USA, 2006, pp. 757-764, doi: 10.1109/WSC.2006.323156.
Abstract
Let (X n :n ges 0) be a sequence of iid rv's with mean zero and finite variance. We describe an efficient state-dependent importance sampling algorithm for estimating the tail of S n = X 1 + ... + X n in a large deviations framework as n - infin. Our algorithm can be shown to be strongly efficient basically throughout the whole large deviations region as n - infin (in particular, for probabilities of the form P (S n > kn) as k > 0). The techniques combine results of the theory of large deviations for sums of regularly varying distributions and the basic ideas can be applied to other rare-event simulation problems involving both light and heavy-tailed features
Authors
Jose H Blanchet, Jingchen Liu
Publication date
2006/12/3
Conference
Proceedings of the 2006 Winter Simulation Conference
Pages
757-764
Publisher
IEEE
