Operations Research
Robust multiple comparisons under common random numbers
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Evaluating adaptive signal control using CORSIM
Proceedings of the 30th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Comparisons with a Standard in Simulation Experiments
Management Science
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Combined ranking and selection with control variates
Proceedings of the 38th conference on Winter simulation
An adaptive procedure for estimating coherent risk measures based on generalized scenarios
Proceedings of the 38th conference on Winter simulation
Selecting the best simulated system with weighted control-variate estimators
Mathematics and Computers in Simulation
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Ranking and selection procedures (R&S) were developed by statisticians to search for the best among a small collection of populations or treatments, where the “best” treatment is typically the one with the largest or smallest expected(long-run average) response. R&S procedures have been successfully extended to address situations that are encountered in stochastic simulation of alternative system designs, including unequal variances across alternatives, dependence both within the output of each system and across the outputs from alternative systems, and large numbers of alternatives to compare. In nearly all cases the estimator of the expected response is a (perhaps generalized) sample mean of the output of interest. In this article we derive R&S procedures that employ control-variate estimators instead of sample means. Control variates can be much more statistically efficient than sample means, leading to R&S procedures that are correspondingly more efficient. We also consider the related problem of estimating the expected value of the best (as opposed to the selected) system design.