Two ARMA-Based Confidence-Interval Procedures for the Analysis of Simulation Output.
MICHIGAN UNIV ANN ARBOR GRADUATE SCHOOL OF BUSINESS ADMINISTRATION
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Two methods are presented for building interval estimates on the mean of a stationary stochastic process. Both methods fit an autoregressive moving-average ARMA model to observations on the process. The model is used to estimate the variance of the sample mean and the applicable degrees of freedom of the t statistic. Fitting of the ARMA model is totally automated. The ARMA-based confidence intervals perform well with data generated from ARMA processes. With data generated from queuing-system simulations, the coverage of the confidence intervals is less than satisfactory. It is shown that with queing-system data, sample mean and its estimated standard deviation are strongly positively correlated, and that the residuals of the fitted models are not normally distributed. These factors contribute adversely to the coverage of the confidence-interval procedures with queuing data. Author
- Statistics and Probability