Two-Stage Multiple-Comparison Procedures for Steady-State Simulations

Document Type

Article

Publication Date

1-1-1999

Abstract

Procedures for multiple comparisons with the best are investigated in the context of steady-state simulation, whereby a number k of different systems (stochastic processes) are compared based upon their (asymptotic) means μ i ( i = 1,2, . . ., k). The variances of these (asymptotically stationary) processes are assumed to be unknown and possibly unequal. We consider the problem of constructing simultaneous confidence intervals for μ j≠iμ j ( i = 1,2, . . ., k), which is known as multiple comparisons with the best (MCB). Our intervals are constrained to contain 0, and so are called constrained MCB intervals. In particular, two-stage procedures for construction of absolute- and relative-width confidence intervals are presented. Their validity is addressed by showing that the confidence intervals cover the parameters with probability of at least some user-specified threshold value, as the confidence intervals' width parameter shrinks to 0. The general assumption about the processes is that they satisfy a functional central limit theorem. The simulation output analysis procedures are based on the method of standardized time series (the batch means method is a special case). The techniques developed here extend to other multiple-comparison procedures such as unconstrained MCB, multiple comparisons with a control, and all-pairwise comparisons. Although simulation is the context in this paper, the results naturally apply to (asymptotically) stationary time series.

Identifier

0012334247 (Scopus)

Publication Title

ACM Transactions on Modeling and Computer Simulation

External Full Text Location

https://doi.org/10.1145/301677.301679

ISSN

10493301

First Page

1

Last Page

30

Issue

1

Volume

9

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