On the inefficiency of state-independent importance sampling in the presence of heavy tails

Achal Bassamboo*, Sandeep Juneja, Assaf Zeevi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper proves that there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk with heavy-tailed increments exceeds a "high" threshold before going below zero. Explicit bounds are given on the best asymptotic variance reduction that can be achieved by state-independent schemes.

Original languageEnglish (US)
Pages (from-to)251-260
Number of pages10
JournalOperations Research Letters
Volume35
Issue number2
DOIs
StatePublished - Mar 1 2007

Keywords

  • Asymptotic analysis
  • Heavy tails
  • Importance sampling
  • State-dependent change-of-measure

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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