The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class. In this article we estimate an optimal importance sampling density within a wider semiparametric class of distributions. We show that this semiparametric version of the Cross Entropy method frequently yields efficient estimators. We illustrate the excellent practical performance of the method with numerical experiments and show that for the problems we consider it typically outperforms alternative schemes by orders of magnitude.
# 13-127/III (2013-09-02)
- Zdravko Botev, The University of New South Wales, Sydney, Australia; Ad Ridder, VU University Amsterdam; Leonardo Rojas-Nandayapa, The University of Queensland
- Light-Tailed; Regularly-Varying; Subexponential; Rare-Event Probability; Cross Entropy method, Markov Chain Monte Carlo
- JEL codes:
- C61, C63