An Improved Batch Means Procedure for Simulation Output Analysis

References

• Akaike H. A new look at the statistical model identification. IEEE Trans. Automatic Control (1974) AC-19:716–723Crossref, Google Scholar
• Alexopoulos C., Seila A. F. Advanced methods for simulation output analysis. Handbook of Simulation (1998) (John Wiley & Sons, New York) 225–272Crossref, Google Scholar
• Anderson O. D.Time Series Analysis and Forecasting: The Box-Jenkins Approach (1976) (Butterworth & Co., London, U.K) Google Scholar
• Billingsley P.Convergence of Probability Measures (1968) (John Wiley & Sons, New York) Google Scholar
• Box G. E. P., Jenkins G. M., Reinsel G. C.Time Series Analysis: Forecasting and Control (1994) 3rd ed.(Prentice Hall, Englewood Cliffs, NJ) Google Scholar
• Chen E. J., Kelton W. D. A stopping procedure based on phi-mixing conditions. Proc. 2000 Winter Simulation Conf. (2000) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 617–626Available online at http://www.informs-cs. org/wsc00papers/083.PDF retrieved August 13, 2002Crossref, Google Scholar
• Chien C. Small sample theory for steady state confidence intervals. (1989) (Department of Operations Research, Stanford University, Stanford, CA) Google Scholar
• Conway R. W. Some tactical problems in digital simulation. Management Sci. (1963) 10(1):47–61Link, Google Scholar
• Fishman G. S. Grouping observations in digital simulation. Management Sci. (1978) 24(5):510–521Link, Google Scholar
• Fishman G. S. LABATCH.2 for analyzing sample path data (online). (1998) (Department of Operations Research, University of North Carolina, Chapel Hill, NC) . Available at http://www.unc.edu/ depts/or/downloads/tech_reports/fishman/uncor97-04.ps retrieved December 27, 2001Google Scholar
• Fishman G. S., Yarberry L. S. An implementation of the batch means method. INFORMS J. Comput. (1997) 9(3):296–310Link, Google Scholar
• Fox B. L., Goldsman D., Swain J. J. Spaced batch means. Oper. Res. Lett. (1991) 10:255–263Crossref, Google Scholar
• IMSL Problem Solving Software SystemsUser's Manual: STAT/LIBRARY (1987) (IMSL, Houston, TX) Google Scholar
• Law A. M., Carson J. S. A sequential procedure for determining the length of a steady-state simulation. Oper. Res. (1979) 27(5):1011–1025Link, Google Scholar
• Malkovich J. F., Afifi A. A. On tests for multivariate normality. J. Amer. Statist. Assoc. (1973) 68:176–179Crossref, Google Scholar
• Royston J. P. An extension of Shapiro and Wilk's W test for normality to large samples. Appl. Statist. (1982a) 31(2):115–124Crossref, Google Scholar
• Royston J. P. Algorithm AS 181. The W test for normality. Appl. Statist. (1982b) 31:176–180Crossref, Google Scholar
• Steiger N. M. Improved batching for confidence interval construction in steady state simulation. (1999) (Doctoral dissertation, Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . Available online at 〈http://www.lib.ncsu.edu/etd/public/etd-19231992992670/etd.pdf retrieved June 18, 2000Crossref, Google Scholar
• Steiger N. M., Wilson J. R. Improved batching for confidence interval construction in steady-state simulation. Proc. 1999 Winter Simulation Conf (1999) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 442–451Available online at http://www.informs-cs.org/wsc99papers/061.PDF retrieved June 18, 2000Crossref, Google Scholar
• Steiger N. M., Wilson J. R. Experimental performance evaluation of batch-means procedures for simulation output analysis. Proceedings of the 2000 Winter Simulation Conf. (2000) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 627–636Available online at http://www.informs-cs.org/wsc00papers/084.PDF retrieved January 6, 2001Crossref, Google Scholar
• Steiger N. M., Wilson J. R. Convergence properties of the batch means method for simulation output analysis. INFORMS J. Comput. (2001) 13(4):277–293Link, Google Scholar
• Steiger N. M., Wilson J. R. ASAP software and user's manual (online). (2002) (Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . Available online at 〈ftp://ftp.ncsu.edu/pub/eos/pub/jwilson/installasap.exe〉 retrieved June 23, 2002Google Scholar
• Steiger N. M., Lada E. K., Wilson J. R., Alexopoulos C., Goldsman D., Zouaoui F. ASAP2: An improved batch means procedure for simulation output analysis. Proc. 2002 Winter Simulation Conf. (2002) (Institute of Electrical and Electronics Engineers, Piscat-away, NJ) . ForthcomingCrossref, Google Scholar
• Stuart A., Ord J. K.Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory (1994) 6th ed.(Edward Arnold, London, U.K) Google Scholar
• Tew J. D., Wilson J. R. Validation of simulation analysis methods for the Schruben-Margolin correlation-induction strategy. Oper. Res. (1992) 40(1):87–103Link, Google Scholar
• von Neumann J. Distribution of the ratio of the mean square successive difference to the variance. Ann. Math. Statist. (1941) 12:367–395Crossref, Google Scholar
• Young L. C. On randomness in ordered sequences. Ann. Math. Statist. (1941) 12:293–300Crossref, Google Scholar