Importance sampling for sums of random variables with regularly varying tails

Paul Dupuis, Kevin Leder, Hui Wang

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Importance sampling is a variance reduction technique for efficient estimation of rare-event probabilities by Monte Carlo. For random variables with heavy tails there is little consensus on how to choose the change of measure used in importance sampling. In this article we study dynamic importance sampling schemes for sums of independent and identically distributed random variables with regularly varying tails. The number of summands can be random but must be independent of the summands. For estimating the probability that the sum exceeds a given threshold, we explicitly identify a class of dynamic importance sampling algorithms with bounded relative errors. In fact, these schemes are nearly asymptotically optimal in the sense that the second moment of the corresponding importance sampling estimator can be made as close as desired to the minimal possible value.

Original languageEnglish (US)
Article number1243995
JournalACM Transactions on Modeling and Computer Simulation
Volume17
Issue number3
DOIs
StatePublished - Jul 1 2007

Keywords

  • Asymptotically optimal relative error
  • Bounded relative error
  • Dynamic importance sampling
  • Rare events
  • Regularly varying tails
  • Variance reduction

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