On an initial transient deletion rule with rigorous theoretical support

Proceedings - Winter Simulation Conference

Volumn , Issue , 2006, Pages 186-191

  Awad, Hernán P ^a   Glynn, Peter W ^b  

^a UNIVERSITY OF MIAMI   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

(PL) PROPERTIES; DESIRABLE PROPERTIES; PARALLEL PROCESSING;

EID: 46149085200     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2006.323072     Document Type: Conference Paper

References (31)

1. Aldous, D. 1991. Meeting times for independent Markov chains. Stochastic Processes and their Applications 38:185-193.
2. Aldous, D., and P. Diaconis. 1986. Shuffling cards and stopping times. American Mathematical Monthly 93:333-348.
3. Awad, H., and P. W. Glynn. 2006. ACM Transactions on Modelling and Computer Simulation. To appear.
4. Brémaud, P. 1999. Markov chains, Gibbsfields, Monte Carlo Simulation and queues. New York: Springer-Verlag.
5. Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10:47-61.
6. Cowles, M. K., and B. P. Carlin. 1996. Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91 (434): 883-904.
7. Diaconis, P., and L. Saloff-Coste. 1993. Comparison theorems for reversible markov chains. The Annals of Applied Probability 3.
8. Diaconis, P., and D. Stroock. 1991. Geometric bounds for eigenvalues of Markov chains. The Annals of Applied Probability 1.
9. Fill, J. A. 1991. Eigenvalue bounds on convergence to stationarity for nonreversible Markov chains, with an application to the exclusion process. The Annals of Applied Probability 1 (1): 62-87.
10. Fishman, G. 1971. Estimating sample size in computer simulation experiments. Management Science 18:21-37.
11. Gafarain, A., C. Ancker, and T. Morisaku. 1978. Evaluation of commonly used rules for detecting steady-state in computer simulation. Naval Research Logistics 25:511-529.
12. Glynn, P. W. 1984. Some asymptotic formulas for Markov chains with applications to simulation. Journal of Statistical Computation and Simulation 19:97-112.
13. Glynn, P. W., and P. Heidelberger. 1991. Analysis of initial transient deletion for replicated steady-state simulations. Operations Research Letters 10 (8): 437 - 443.
14. Glynn, P. W., and D. L. Iglehart. 1987. A new bias deletion rule. In Proceedings of the 1987 Winter Simulation Conference, ed. A. Thesen, H. Grant, and D. Kelton.
15. Jackway, P., and B. de Silva. 1992. A methodology for initialisation bias reduction in computer simulation output. Asia Pacific Journal of Operational Research 9:85-98.
16. Linton, J. R., and C. M. Harmonosky. 2002. A comparison of selective initialization bias elimination methods. In Proceedings of the 2002 Winter Simulation Conference, ed. E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, 1951-1957.
17. Mengersen, K., C. Robert, and C. Guihenneuc-Jouyaux. 1999. MCMC convergence diagnostics: a review. In Bayesian Statistics 6, ed. J. Bernardo, J. Berger, A. Dawid, and A. Smith. Oxford University Press.
18. Meyn, S., and R. Tweedie. 1994. Computable bounds for geometric convergence rates of Markov chains. Annals of Applied Probability 4:981-1011.
19. Pawlikowski, K. 1990. Steady-state simulation of queuing processes: a survey of problems and solutions. ACM Computing Surveys 22:123-170.
20. Richet, Y., O. Jacquet, and X. Bay. 2003. Automated suppression of the initial transient in Monte Carlo calculations based on stationarity detection using the Brownian bridge theory. In Proceedings of the The 7th International Conference on Nuclear Criticality Safety (ICNC2003), 63. Tokai-mura, Japan.
21. Robinson, S. 2002. A statistical process control approach for estimating the warm-up period. In Proceedings of the 2002 Winter Simulation Conference, ed. E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, 439-446.
22. Rosenthal, J. S. 1995a. Minorization conditions and convergence rates for Markov chain Monte Carlo. Journal of the American Statistical Association 90:558-566.
23. Rosenthal, J. S. 1995b. Rates of convergence for Gibbs sampling for variance component models. Annals of Statistics 23:740-761.
24. Rosenthal, J. S. 1996. Analysis of the Gibbs sampler for a model related to James-Stein estimators. Statistics and Computers 6:269-275.
25. Roth, E., and A. Rutan. 1985. A relaxation time approach for reducing initialization bias in Simulation. In Proceedings of the 18th Annual Symposium on Simulation, 189-203.
26. Schruben, L. 1982. Detecting initialization bias in simulation output. Operations Research 30:569-590.
27. Schruben, L., H. Singh, and L. Tierney. 1983. Optimal tests for initialization bias in simulation output. Operations Research 31:1167-1178.
28. Vassilacopoulos, G. 1989. Testing for initialization bias in simulation output. Simulation 52 (4): 151-153.
29. Welch, P. 1983. The statistical analysis of simulation results. In The Computer Performance Modeling Handbook, ed. S. Lavenberg, 268-328. New York: Academic Press.
30. Wilson, J., and A. Pritsker. 1978a. Evaluation of startup policies in simulation experiments. Simulation 31:79-89.
31. Wilson, J., and A. Pritsker. 1978b. A survey of research on the simulation startup problem. Simulation 31:55-59.