An Autoregressive Method for Simulation Output Analysis.
STANFORD UNIV CA DEPT OF OPERATIONS RESEARCH
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As the use of computer simulation becomes more important in the study of complex phenomena, the need to develop theoretically sound and computationally efficient methods for simulation output analysis becomes more pressing. The autoregressive method proposed in this paper uses techniques developed for time series analysis to provide both point and interval estimates for parameters associated with the steady-state distribution. The major advantage of the autoregressive method is obvious. It serves as a black box users provide the simulation output sequence, the black box will produce results automatically. Furthermore, it seems that the autoregressive method applies to a much broader class of stochastic processes than the regenerative method does. With the generalization to multidimensional processes, the method enables us to apply variance reduction techniques to get more accurate point estimates along with more precise interval estimates. The disadvantages of the autoregressive method are that the covariance matrix obtained by the autoregressive method is just an approximation for the covariance matrix present in the central limit theorem used to construct confidence intervals, and the assumptions put on the system are stricter than we would like.
- Statistics and Probability