A principal-components approach to assign confidence intervals in steady-state simulation

This paper presents an approach to the assignment of a confidence interval to the mean of a stream of autocorrelated output data from a steady-state simulation run. Based on the principal-components analysis method, the approach is to derive a linear transformation of the data that yields approximate independence of the transformed data. To aid convergence to normality (on which the confidence interval is based) and to keep the dimension of the transformation reasonable, the original output data are batched prior to performing the transformation. The approach is fairly simple to understand and to implement, and experimental results indicate that it may perform better than the non-overlapping batch means method in terms of coverage with any number of batches.