Integrated Variance Reduction Strategies

Avramidis, A.N.; Wilson, J.R.

doi:10.1109/wsc.1993.718084

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In Section 5 we summarize the main findings of this work, and we recommend directions for future research. Although this paper is based on Avramidis (1993), some of our results were also presented in Avramidis and Wilson (1993b).…”

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confidence: 99%

Integrated Variance Reduction Strategies for Simulation

Avramidis¹,

Wilson

1996

Operations Research

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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; and 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.

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confidence: 99%

Integrated Variance Reduction Strategies for Simulation

Avramidis¹,

Wilson

1996

Operations Research

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Simulations of communication systems via integrated variance reduction techniques

Bohdanowicz

Weber

The 57th IEEE Semiannual Vehicular Technology Conference, 2003. VTC 2003-Spring.

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Combining antithetic variates and control variates in simulation experiments

Yang

Liou

1996

ACM Trans. Model. Comput. Simul.

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Antithetic variates and control variates are two well-known variance reduction techniques. We consider combining antithetic variates and control variates to estimate the mean response in a stochastic simulation experiment. When applying antithetic variates to generate control variates across paired replications, we show that the integrated control-variate estimator is unbiased and yields, under the assumption of common correlations induced for all control variates, a smaller variance than the conventional control-variate estimator without using antithetic variates. We examine the proposed estimator and two alternative integrated control-variate estimators when applying antithetic variates on control variates and show that the proposed estimator is the optimal integrated control-variate estimator. We implement these three integrated control-variate estimators and the conventional control-variate estimator in a simulation model of a stochastic network to evaluate the performance of each control-variate estimator. Empirical results show that the proposed estimator outperforms the other control-variate estimators.

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Integrated Variance Reduction Strategies

Cited by 3 publications

References 7 publications

Integrated Variance Reduction Strategies for Simulation

Integrated Variance Reduction Strategies for Simulation

Simulations of communication systems via integrated variance reduction techniques

Combining antithetic variates and control variates in simulation experiments

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