New confidence interval estimators using standardized time series
Management Science
Properties of standardized time series weighted area variance estimators
Management Science
Simulation output analysis using standardized time series
Mathematics of Operations Research
Confidence intervals using orthonormally weighted standardized time series
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Improving standardized time series methods by permuting path segments
Proceedings of the 33nd conference on Winter simulation
Cramer-Von Mises Variance Estimators for Simulations
Operations Research
Permuted Standardized Time Series for Steady-State Simulations
Mathematics of Operations Research
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Calvin and Nakayama previously introduced permuting as a way of improving existing standardized time series methods. The basic idea is to split a simulated sample path into non-overlapping segments, permute the segments to construct a new sample path, and apply a standardized time series scaling function to the new path. Averaging over all permuted paths yields the permuted estimator. This paper discusses applying permutations to the weighted area estimator of Goldsman and Schruben. Empirical results seem to indicate that compared to not permuting, permuting can reduce the length and variability of the resulting confidence interval half widths but with additional computational overhead and some degradation in coverage; however, the decrease in coverage is not as bad as with batching.