Multiple comparisons with the best using common random numbers for steady-state simulations

Document Type

Article

Publication Date

4-1-2000

Abstract

Suppose that there are k≥2 different systems (i.e., stochastic processes), where each system has an unknown steady-state mean performance. We consider the problem of running a single-stage simulation using common random numbers to construct simultaneous confidence intervals for μi-maxj≠iμj,i=1,2,...,k. This is known as multiple comparisons with the best (MCB). Under an assumption that the stochastic processes representing the simulation output of the different systems satisfy a functional central limit theorem, we prove that our confidence intervals are asymptotically valid (as the run lengths of the simulations of each system tends to infinity). We develop algorithms for two different cases: when the asymptotic covariance matrix has sphericity, and when the covariance matrix is arbitrary. © 2000 Elsevier Science B.V.

Identifier

0008548796 (Scopus)

Publication Title

Journal of Statistical Planning and Inference

External Full Text Location

https://doi.org/10.1016/S0378-3758(99)00064-6

ISSN

03783758

First Page

37

Last Page

48

Issue

1-2

Volume

85

Grant

421180

Fund Ref

National Science Foundation

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