Confidence interval estimation using quasi-independent sequences

A Quasi-Independent (QI) subsequence is a subset of time series observations obtained by systematic sampling. Because the observations appear to be independent, as determined by the runs tests, classical statistical techniques can be used on those observations directly. This paper discusses implementation of a sequential procedure to determine the simulation run length to obtain a QI subsequence, and the batch size for constructing confidence intervals for an estimator of the steady-state mean of a stochastic process. The proposed QI procedures increase the simulation run length and batch size progressively until a certain number of essentially independent and identically distributed samples are obtained. The only (mild) assumption is that the correlations of the stochastic-process output sequence eventually die off as the lag increases. An experimental performance evaluation demonstrates the validity of the QI procedure.