Stochastics and Statistics A wavelet - based spectral procedure for steady - state simulation analysis q

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 logspectrum 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. 2005 Elsevier B.V. All rights reserved.