Blanchet, J., Chen, L., Dong, J. (2022). Exact Sampling for the Maximum of Infinite Memory Gaussian Processes. In: Botev, Z., Keller, A., Lemieux, C., Tuffin, B. (eds) Advances in Modeling and Simulation. Springer, Cham. https://doi.org/10.1007/978-3-031-10193-9_3

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Abstract

We develop an exact sampling algorithm for the all-time maximum of Gaussian processes with negative drift and general covariance structures. In particular, our algorithm can handle non-Markovian processes even with long-range dependence. Our development combines a milestone-event construction with rare-event simulation techniques. This allows us to find a random time beyond which the running time maximum will never be reached again. The complexity of the algorithm is random but has finite moments of all orders. We also test the performance of the algorithm numerically.

Authors
Jose Blanchet, Lin Chen, Jing Dong
Publication date
2022/12/1
Book
Advances in Modeling and Simulation: Festschrift for Pierre L'Ecuyer
Pages
41-63
Publisher
Springer International Publishing