Approximation-assisted point estimation

Barry L. Nelson, Bruce W. Schmeiser*, Michael R. Taaffe, Jin Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We investigate three alternatives for combining a deterministic approximation with a stochastic simulation estimator: (1) binary choice, (2) linear combination, and (3) Bayesian analysis. Making a binary choice, based on compatibility of the simulation estimator with the approximation, provides at best a 20% improvement in simulation efficiency. More effective is taking a linear combination of the approximation and the simulation estimator using weights estimated from the simulation data, which provides at best a 50% improvement in simulation efficiency. The Bayesian analysis yields a linear combination with weights that are a function of the simulation data and the prior distribution on the approximation error; the efficiency depends upon the quality of the prior distribution.

Original languageEnglish (US)
Pages (from-to)109-118
Number of pages10
JournalOperations Research Letters
Volume20
Issue number3
DOIs
StatePublished - Mar 1997

Keywords

  • Biased estimation
  • Control variates
  • Monte Carlo
  • Simulation

ASJC Scopus subject areas

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

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