Abstract
The cross entropy is a well-known adaptive importance sampling method which requires estimating an optimal importance sampling distribution within a parametric class. In this paper we analyze an alternative version of the cross entropy, where the importance sampling distribution is selected instead within a general semiparametric class of distributions. We show that the semiparametric cross entropy method delivers efficient estimators in a wide variety of rare-event problems. We illustrate the favourable performance of the method with numerical experiments.
Original language | English |
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Pages (from-to) | 633-649 |
Journal | Journal of Applied Probability |
Volume | 53 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2016 |