Open Access
December 1997 Multiple-comparison procedures for steady-state simulations
Marvin K. Nakayama
Ann. Statist. 25(6): 2433-2450 (December 1997). DOI: 10.1214/aos/1030741080


Suppose that there are $k \geq 2$ different systems (i.e., stochastic processes), where each system has an unknown steady-state mean performance and unknown asymptotic variance. We allow for the asymptotic variances to be unequal and for the distributions of the k systems to be different. We consider the problem of running independent, single-stage simulations to make multiple comparisons of the steady-state means of the different systems. We derive asymptotically valid (as the run lengths of the simulations of the systems tend to infinity) simultaneous confidence intervals for each of the following problems: all pairwise comparisons of means, all contrasts, multiple comparisons with a control and multiple comparisons with the best. Our confidence intervals are based on standardized time series methods, and we establish the asymptotic validity of each under the sole assumption that the stochastic processes representing the simulation output of the different systems satisfy a functional central limit theorem. Although simulation is the context of this paper, the results naturally apply to (asymptotically) stationary time series.


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Marvin K. Nakayama. "Multiple-comparison procedures for steady-state simulations." Ann. Statist. 25 (6) 2433 - 2450, December 1997.


Published: December 1997
First available in Project Euclid: 30 August 2002

zbMATH: 0894.68178
MathSciNet: MR1604477
Digital Object Identifier: 10.1214/aos/1030741080

Primary: 62J15 , 68U20
Secondary: 60F17 , 62M10 , 65C05

Keywords: functional central limit theorem , Multiple comparisons , output analysis , standardized time series , Stochastic simulation , time series

Rights: Copyright © 1997 Institute of Mathematical Statistics

Vol.25 • No. 6 • December 1997
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