Philip Heidelberger
- 1 Bartlett, M.S. and Kendall, D.G. The statistical analysis of variance heterogeneity and the logarithmic transformation. J. Roy. Statist. Soc. (Suppl.), 8, 1 (1946), 128-138.Google Scholar
- 2 Baskett, F., Chandy, K.M., Muntz, R.R., and Palacios, F.G. Open, closed, and mixed networks of queues with different classes of jobs. J. ACM 22, 2 (April 1975), 248-260. Google ScholarDigital Library
- 3 Brillinger, D.R. Time Series, Data Analysis and Theory. Holt, Rinehart and Winston, New York, 1975. Google ScholarDigital Library
- 4 Cooley, J.W., Lewis, P.A.W., and Welch, P.D. The finite Fourier transform. IEEE Trans. Audio Electroacoust. 17, 2 (June 1969), 77- 85.Google ScholarCross Ref
- 5 Cooley, J.W., Lewis, P.A.W., and Welch, P.D. The application of the fast Fourier transform to the estimation of spectra and cross spectra. J. Sound Vib. 12, 3 (1970), 339-352.Google ScholarCross Ref
- 6 Duket, S.D. and Pritsker, A.A.B. Examination of simulation output using spectral methods. Math. Compt. Simulation. 20, (1978), 53-60.Google ScholarCross Ref
- 7 Fishman, G.S. Principles of Discrete Event Simulation. John Wiley and Sons, New York, 1978. Google ScholarDigital Library
- 8 Iglehart, D.L. The regenerative method for simulation analysis. In Current Trends in Programming Methodology, Vol. 111: Software Engineering. K.M. Chandy and R.T. Yeh (Eds,), Prentice-Hall, Englewood Cliffs, N.J., 1978.Google Scholar
- 9 Jenkins, G.M. and Watts, D.G. SpectralAnalysis and Its Applications. Holden Day, San Francisco, 1968.Google Scholar
- 10 Lavenberg, S.S. and Sauer, C.H. Sequential stopping rules for the regenerative method of simulation. IBM J. Res. Develop. 21, 6 (Nov. 1977), 545-558.Google ScholarDigital Library
- 11 Law, A.M. and Carson, J.S. A sequential procedure for determining the length of a steady-state simulation. Operations Res. 27, 5 (Sept.-Oct. 1979), 1011-1025.Google Scholar
- 12 Moeller, T.L. and Welch, P.D. A spectral based technique for generating confidence intervals from simulation outputs. Proc. 1977 Winter Simulation Conf. Vol. 1, (1977) 177-184. Google ScholarDigital Library
- 13 Mood, A.M., Graybill, F.A., and Boes, D.C. Introduction to the Theory of Statistics, 3rd Ed. McGraw-Hill, New York, 1974.Google Scholar
- 14 Olshen, R.A. Asymptotic properties of the periodogram of a discrete stationary process. J. Appl. Probability 4, (1967), 508-528.Google ScholarCross Ref
- 15 Sauer, C.H. and MacNair, E.A. Queueing network software for systems modelling. Software Practice and Experience 9, 5 (May 1979), 360-380.Google ScholarCross Ref
- 16 Wahba, G. Automatic smoothing of the log periodogram. J. Am. Statist. Assoc. 75, 369 (March 1980), 122-132.Google ScholarCross Ref
- 17 Walker, A.M. Some asymptotic results for the periodogram of a stationary time series. J. Austral. Math. Soc. 5, (1965), 107-128.Google ScholarCross Ref
Index Terms
- A spectral method for confidence interval generation and run length control in simulations
Recommendations
Confidence Interval Estimation for the Variance Parameter of Stationary Processes
Asymptotic confidence interval estimators of the variance parameter ý2 = lim n â ∞ n Var1/ n â n i = 1 X i are described in this paper for observations X 1, X 2,', X n from a strictly stationary phi-mixing stochastic process. They are based on ...
To batch or not to batch?
When designing steady-state computer simulation experiments, one may be faced with the choice of batching observations in one long run or replicating a number of smaller runs. Both methods are potentially useful in the course of undertaking simulation ...
Weighted Batch Means for Confidence Intervals in Steady-State Simulations
We propose a new procedure for providing confidence-interval estimators of the mean of a covariance-stationary process. The procedure, a modification of the method of batch means, is an improvement over existing methods when the process displays strong ...
Comments