Permuted derivative and importance-sampling estimators for regenerative simulations

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In a previous paper we introduced a new variance-reduction technique for regenerative simulations based on permuting regeneration cycles. In this paper we apply this idea to new classes of estimators. In particular, we derive permuted versions of likelihood-ratio derivative estimators for steady-state performance measures, importance-sampling estimators of the mean cumulative reward until hitting a set of states, and Tin estimators for steady-state ratio formulas. Empirical results are presented showing that modest to significant variance reductions can be obtained.

Original languageEnglish (US)
Pages (from-to)390-414
Number of pages25
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Jul 16 2004

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


  • Regenerative method
  • Simulation
  • Variance reduction


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