The correlation between mean and variance estimators
WSC '85 Proceedings of the 17th conference on Winter simulation
Selecting and ordering populations: a new statistical methodology
Selecting and ordering populations: a new statistical methodology
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Ranking and selection in simulation
WSC '83 Proceedings of the 15th conference on Winter Simulation - Volume 2
Application of an optimization procedure to steady-state simulation
WSC '84 Proceedings of the 16th conference on Winter simulation
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Restricted subset selection for normal populations with unknown and unequal variances
WSC '84 Proceedings of the 16th conference on Winter simulation
Tutorial on indifference-zone normal means ranking and selection procedures
WSC '86 Proceedings of the 18th conference on Winter simulation
Selection procedures with standardized time series variance estimators
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Batch-size effects on simulation optimization using multiple comparisons with the best
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Ranking and selection for steady-state simulation
Proceedings of the 32nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We study the problem of determining that one of k stationary simulated processes which has the largest mean. We adapt for use in the simulation environment a ranking and selection procedure due to Dudewicz and Dalal (1975). In order to implement this procedure, it is necessary to estimate the process variance of each of the k simulated systems; variance estimators arising from the theory of standardized time series are used for this purpose.