Convergence Properties of the Batch Means Method for Simulation Output Analysis

References

  • Anderson T. W.The Statistical Analysis of Time Series (1971) (John Wiley & Sons, New York) 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) Third Edition(Holden-Day, San Francisco) Google Scholar
  • Daley D. J. The serial correlation coefficients of waiting times in a stationary single server queue. Journal of the Australian Mathematical Society (1968) 8:683–699CrossrefGoogle Scholar
  • Fishman G. S. Grouping observations in digital simulation. Management Science (1978) 24:510–521LinkGoogle Scholar
  • Glynn P. W., Whitt W. Estimating the asymptotic variance with batch means. Operations Research Letters (1991) 10:431–435CrossrefGoogle Scholar
  • Hadley G.Linear Algebra (1961) (Addison-Wesley, Reading, MA) Google Scholar
  • Hanselman D., Littlefield B.The Student Edition of MATLAB: Version 5 (1997) (User's Guide/The MathWorks, Inc. Prentice-Hall, Upper Saddle River, NJ) Google Scholar
  • Hazen G. B., Pritsker A. A. B. Formulas for the variance of the sample mean in finite state Markov processes. Journal of Statistical Computation and Simulation (1980) 12:25–40CrossrefGoogle Scholar
  • Kang K., Goldsman D. The correlation between mean and variance estimators in computer simulation. IIE Transactions (1990) 22:15–23CrossrefGoogle Scholar
  • Karlin S., Taylor H. M.A First Course in Stochastic Processes (1975) Second Edition(Academic Press, New York) Google Scholar
  • Kemeny J. G., Snell J. L.Finite Markov Chains (1960) (D. Van Nostrand and Co., Princeton, NJ) Google Scholar
  • Law A. M., Kelton W. D.Simulation Modeling and Analysis (2000) 3d ed.(McGraw-Hill, New York) Google Scholar
  • Muñoz D. F., Glynn P. W. A batch means methodology for estimation of a nonlinear function of a steady-state mean. Management Science (1997) 43:1121–1135LinkGoogle Scholar
  • Sargent R. G., Kang K., Goldsman D. An investigation of finite-sample behavior of confidence interval estimators. Operations Research (1992) 40:898–913LinkGoogle Scholar
  • Song W.-M., Schmeiser B. W. Optimal mean-squared-error batch sizes. Management Science (1995) 41:110–123LinkGoogle Scholar
  • Steiger N. M.Improved batching for confidence interval construction in steady-state simulation (1999) (Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . http://www.lib.ncsu.edu/etd/public/etd-19231992992670/etd.pdf, Ph.D. Dissertation, [accessed January 15, 2001]CrossrefGoogle Scholar
  • Steiger N. M., Wilson J. R., Farrington P. A., Nembhard H. B., Sturrock D. T., Evans G. W. Improved batching for confidence interval construction in steady-state simulation. Proceedings of the 1999 Winter Simulation Conference (1999) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 442–451 http://www.informs-cs.org/wsc99papers/061.PDF [accessed May 11, 2000]CrossrefGoogle Scholar
  • Steiger N. M., Wilson J. R., Barton R. R., Joines J. A., Kang K., Fishwick P. A. Experimental performance evaluation of batch-means procedures for simulation output analysis. Proceedings of the 2000 Winter Simulation Conference (2000a) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 627–636 http://www.informs-cs.org/wsc00papers/084.PDF [accessed January 6, 2000]CrossrefGoogle Scholar
  • Steiger N. M., Wilson J. R.An improved batch means procedure for simulation output analysis (2000b) (Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . Technical Report, ftp://ftp.ncsu.edu/pub/eos/pub/jwilson/asaporv12.pdf [accessed June 18, 2000]Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.