Multiple comparison procedures
Multiple comparison procedures
Selecting and ordering populations: a new statistical methodology
Selecting and ordering populations: a new statistical methodology
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Ranking and Selection for Steady-State Simulation: Procedures and Perspectives
INFORMS Journal on Computing
Comparisons with a Standard in Simulation Experiments
Management Science
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
Work smarter, not harder: guidelines for designing simulation experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Work smarter, not harder: guidelines for designing simulation experiments
Proceedings of the 38th conference on Winter simulation
Work smarter, not harder: guidelines for designing simulation experiments
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)
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
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This tutorial discusses some statistical procedures for selecting the best of a number of competing systems. The term "best" may refer to that simulated system having, say, the largest expected value or the greatest likelihood of yielding a large observation. We describe various procedures for finding the best, some of which assume that the underlying observations arise from competing normal distributions, and some of which are essentially nonparametric in nature. In each case, we comment on how to apply the above procedures for use in simulations.