Abstract
Multiple-comparison procedures are useful for comparing the performance of competing systems via simulation. In this paper we extend a particular multiple-comparison procedure, multiple comparisons with the best, to steady-state simulation by using an autoregressive-output-analysis method.
- 1 A~IKE, H. Maximum likelihood identification of Gaussian auto-regressive moving average process. B~ometrtka 60, 2 (Aug. 1973), 255-266.Google Scholar
- 2 Box, G. E. P., ~u JENKINS, G. M. Time Series Analysis. Rev. ed. Holden-Day, San Francisco, Calif., 1976.Google Scholar
- 3 FISHM~N, G.S. Principles of Discrete Event Simulation. Wiley, New York, 1973. Google ScholarDigital Library
- 4 G~~, H. L., KELLY, G. D., AND MCINT~RE, D. D. A new approach to ARMA modelling. Commun. Stat. B7 (1978), 1-77.Google Scholar
- 5 HANNa, E. J. Multiple Time Series. Wiley, New York, 1970.Google Scholar
- 6 HANNAN, E. J., McDOUGALL, A. J., AND POSKITT, D.S. Recursive estimation of autoregressions. J. Roy. Stat. Soc. B51, 2 (1989), 217 233.Google Scholar
- 7 HEMERL~, E. M., AND DAVIS M. H. A. Strong consistency of the PLS criterion for order determination of autoregressive processes. Ann. Stat. 17, 2 (1989), 941 946.Google Scholar
- 8 HOCHBERG, Y., AND TAMHANE, A. C. Multiple Comparison Procedures. Wiley, New York, 1987. Google ScholarDigital Library
- 9 Hsu, J. C. Constrained simultaneous confidence intervals for multiple comparisons with the best. Ann. Stat. 12, 3 (1984), 1136 1144.Google ScholarCross Ref
- 10 Hsu, J. C. Ranking and selection and multiple comparisons with the best. In Design of Experiments: Ranking and Selection, T. J. Santner and A. C. Tamhane, Eds. Marcel Dekker, New York, 1984, chap. 3.Google Scholar
- 11 Hsu, J. C, AND NELSON, B.L. Optimization over a finite number of system designs with one-stage sampling and multiple comparisons with the best. In Proceedings of the 1988 Winter Simulation Conference (San Diego, Calif., Dec. 12-14). ACM/IEEE, New York, 1988, 451-457. Google ScholarDigital Library
- 12 NELSON, B. L., AND HSU, J. C. Control-variate models of common random numbers for multiple comparisons with the best. Manage. Sct. To be published. Google ScholarDigital Library
- 13 RISSANEN, J. Order estimation by accumulated prediction errors. In Essays in Time Series and Allied Processes. J. Gani and M. B. Priestly, Eds. J. Appl. Prob. 23A (1986), 55-61.Google Scholar
- 14 YUAN, M., AND NELSON, B. L. Autoregressive output analysis methods revisited. Working Paper Ser. 1991-007, Dept. of Industrial and Systems Engineering, Ohio State Univ., Columbus, 1991.Google Scholar
Index Terms
- Multiple comparisons with the best for steady-state simulation
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