Integrated Variance Reduction Strategies for Simulation

Athanassios N. Avramidis and James R. Wilson
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Athanassios N. Avramidis: SABRE Decision Technologies, Paris, France
James R. Wilson: North Carolina State University, Raleigh, North Carolina

Operations Research, 1996, vol. 44, issue 2, 327-346

Abstract: We develop strategies for integrated use of certain well-known variance reduction techniques to estimate a mean response in a finite-horizon simulation experiment. The building blocks for these integrated variance reduction strategies are the techniques of conditional expectation, correlation induction (including antithetic variates and Latin hypercube sampling), and control variates; all pairings of these techniques are examined. For each integrated strategy, we establish sufficient conditions under which that strategy will yield a smaller response variance than its constituent variance reduction techniques will yield individually. We also provide asymptotic variance comparisons between many of the methods discussed, with emphasis on integrated strategies that incorporate Latin hypercube sampling. An experimental performance evaluation reveals that in the simulation of stochastic activity networks, substantial variance reductions can be achieved with these integrated strategies. Both the theoretical and experimental results indicate that superior performance is obtained via joint application of the techniques of conditional expectation and Latin hypercube sampling.

Keywords: simulation; design of experiments: antithetic variates; Latin hypercube sampling; simulation; efficiency: conditioning; control variates; correlation induction; simulation; statistical analysis: combined Monte Carlo methods (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:44:y:1996:i:2:p:327-346

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