Steady-state confidence interval methodology: A forum on theory, practice, and prospects

For the past two decades, much of the attention in the statistical methodology of computer simulation has been motivated by and directed toward what is essentially a single goal: To construct a valid confidence interval for a steady-state parameter of a stochastic process by means of simulation. During this time, a number of approaches have been developed in an attempt to meet this goal, and the purpose of this two-session forum is to gather several of these ideas together for exposition and discussion. Each participant in this forum is actively involved in research in this area, and has agreed to present or co-present one of six methods: Replication, batch means, time series, spectral methods, regenerative methods, and standardized time series. The order chosen represents an attempt to follow the chairperson's impression of the chronology of development.

[1]  L. Schruben,et al.  Asymptotic Properties of Some Confidence Interval Estimators for Simulation Output , 1984 .

[2]  P. Heidelberger,et al.  Adaptive spectral methods for simulation output analysis , 1981 .

[3]  Thomas J. Schriber,et al.  A conceptual framework for research in the analysis of simulation output , 1981, CACM.

[4]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[5]  A. A. Crane,et al.  An introduction to the regenerative method for simulation analysis , 1977 .

[6]  Peter D. Welch,et al.  A Spectral Method for Generating Confidence Intervals from Simulation Outputs , 1977 .

[7]  Henry L. Gray,et al.  A New Approach to ARMA Modeling. , 1978 .

[8]  P. Glynn.  Asymptotic Theory for Nonparametric Confidence Intervals. , 1982 .

[9]  D. Iglehart,et al.  Comparing stochastic systems using regenerative simulation with common random numbers , 1979, Advances in Applied Probability.

[10]  Philip Heidelberger,et al.  A spectral method for confidence interval generation and run length control in simulations , 1981, CACM.

[11]  David R. Cox,et al.  The Theory of Stochastic Processes , 1967, The Mathematical Gazette.

[12]  P. Heidelberger,et al.  A Renewal Theoretic Approach to Bias Reduction in Regenerative Simulations , 1982 .

[13]  Lee W. Schruben,et al.  Detecting Initialization Bias in Simulation Output , 1982, Oper. Res..

[14]  I. Good.  Good Thinking: The Foundations of Probability and Its Applications , 1983 .

[15]  Lee W. Schruben,et al.  Confidence Interval Estimation Using Standardized Time Series , 1983, Oper. Res..