Abstract
We develop WASSP, a wavelet-based spectral method for steady-state simulation analysis. First WASSP determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Next WASSP computes the discrete wavelet transform of the bias-corrected log-smoothed-periodogram of the batch means, using a soft-thresholding scheme to denoise the estimated wavelet coefficients. Then taking the inverse discrete wavelet transform of the thresholded wavelet coefficients, WASSP computes estimators of the batch means log-spectrum and the steady-state variance parameter (i.e., the sum of covariances at all lags) for the original (unbatched) process. Finally by combining the latter estimator with the batch means grand average, WASSP provides a sequential procedure for constructing a confidence interval on the steady-state mean that satisfies user-specified requirements concerning absolute or relative precision as well as coverage probability. An experimental performance evaluation demonstrates WASSP’s effectiveness compared with other simulation analysis methods.
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