Selecting the best system in steady-state simulations using batch means
WSC '95 Proceedings of the 27th conference on Winter simulation
Simulation run length planning for stochastic loss models
WSC '95 Proceedings of the 27th conference on Winter simulation
Two-stage procedures for multiple comparisons with a control in steady-state simulations
WSC '96 Proceedings of the 28th conference on Winter simulation
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To design a stochastic simulation experiment, it is helpful to have an estimate of the simulation run lengths required to achieve desired statistical precision. Preliminary estimates of required run lengths can be obtained by approximating the stochastic model of interest by a more elementary Markov model that can be analyzed analytically. When steady-state quantities are to be estimated by sample means, we often can estimate required run lengths by calculating the asymptotic variance and the asymptotic bias of the sample mean in the Markov model.