Integrated variance reduction strategies for simulation
Avramidis, Athanassios.N. and Wilson, James R. (1996) Integrated variance reduction strategies for simulation. Operations Research, 44, (2), 327-346. (doi:10.1287/opre.44.2.327).
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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|
|Subjects:||Q Science > QA Mathematics
T Technology > T Technology (General)
|Divisions:||Faculty of Social and Human Sciences > Mathematics
|Date Deposited:||13 Apr 2012 08:54|
|Last Modified:||26 Apr 2013 05:37|
|Contributors:||Avramidis, Athanassios.N. (Author)
Wilson, James R. (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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