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Semiparametric cross entropy for rare-event simulation

  • Z. I. Botev (a1), A. Ridder (a2) and L. Rojas-Nandayapa (a3)
Abstract
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.

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Corresponding author
*Postal address: School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia. Email address: botev@unsw.edu.au
** Postal address: School of Mathematics and Physics, The University of Queensland, Brisbane, QLD 4072, Australia.
*** Postal address: Department of Econometrics and Operations Research, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands.
References
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Journal of Applied Probability
  • ISSN: 0021-9002
  • EISSN: 1475-6072
  • URL: /core/journals/journal-of-applied-probability
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