Strong consistency of the variance estimator in steady-state simulation output analysis
Mathematics of Operations Research
Large-sample results for batch means
Management Science
Computational efficiency evaluation in output analysis
Proceedings of the 29th conference on Winter simulation
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Finding feasible systems in the presence of constraints on multiple performance measures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the Winter Simulation Conference
Selecting the best by comparing simulated systems in a group of three
Proceedings of the Winter Simulation Conference
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Kim and Nelson (2005) developed two indifference-zone procedures for steady-state simulation where the goal is to find the system with the largest or smallest expected steady-state performance measure. One of the procedures, called KN++, updates a variance estimate as more observations become available and is proven to be asymptotically valid when there is no dependence across systems (for example, there is no use of common random numbers). Their procedure exhibits significant improvement over other existing procedures for use in steady-state simulation. In this paper, we first present a modification of KN++ that is asymptotically valid with the use of common random numbers. Then, we study how well KN++ works when data within a system are independent and identically distributed, but data between systems may be positively correlated. Specific applications include the finding-the-best problem when (i) the data are normal, and (ii) the data are Bernoulli.