@article{tafazzoli_wilson_lada_steiger_2011, title={Performance of Skart: A Skewness- and Autoregression-Adjusted Batch Means Procedure for Simulation Analysis}, volume={23}, ISSN={["1526-5528"]}, DOI={10.1287/ijoc.1100.0401}, abstractNote={An analysis is given for an extensive experimental performance evaluation of Skart, an automated sequential batch means procedure for constructing an asymptotically valid confidence interval (CI) on the steady-state mean of a simulation output process. Skart is designed to deliver a CI satisfying user-specified requirements on absolute or relative precision as well as coverage probability. Skart exploits separate adjustments to the half-length of the classical batch means CI so as to account for the effects on the distribution of the underlying Student's t-statistic that arise from skewness (nonnormality) and autocorrelation of the batch means. Skart also delivers a point estimator for the steady-state mean that is approximately free of initialization bias. In an experimental performance evaluation involving a wide range of test processes, Skart compared favorably with other steady-state simulation analysis methods—namely, its predecessors ASAP3, WASSP, and SBatch, as well as ABATCH, LBATCH, the Heidelberger–Welch procedure, and the Law–Carson procedure. Specifically, Skart exhibited competitive sampling efficiency and closer conformance to the given CI coverage probabilities than the other procedures, especially in the most difficult test processes.}, number={2}, journal={INFORMS JOURNAL ON COMPUTING}, author={Tafazzoli, Ali and Wilson, James R. and Lada, Emily K. and Steiger, Natalie M.}, year={2011}, pages={297–314} }
@article{lada_wilson_steiger_joines_2007, title={Performance of a wavelet-based spectral procedure for steady-state simulation analysis}, volume={19}, DOI={10.1287/ijoc.1050.0161}, abstractNote={A summary and an analysis are given for an experimental performance evaluation of WASSP, an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process such that the delivered confidence interval satisfies user-specified requirements on absolute or relative precision as well as coverage probability. The experimentation involved three difficult test problems, each with an output process exhibiting some combination of the following characteristics: a long warm-up period, a persistent autocorrelation structure, or a highly nonnormal marginal distribution. These problems were used to compare the performance of WASSP with that of the Heidelberger-Welch algorithm and ASAP3, two sequential procedures based respectively on the methods of spectral analysis and nonoverlapping batch means. Concerning efficiency (required sample sizes) and robustness against the statistical anomalies commonly encountered in simulation studies, WASSP outperformed the Heidelberger-Welch procedure and compared favorably with ASAP3.}, number={2}, journal={INFORMS Journal on Computing}, publisher={Institute for Operations Research and the Management Sciences (INFORMS)}, author={Lada, E. K. and Wilson, J. R. and Steiger, N. M. and Joines, J. A.}, year={2007}, pages={150–160} }
@article{lada_wilson_2006, title={A wavelet-based spectral procedure for steady-state simulation analysis}, volume={174}, ISSN={["0377-2217"]}, DOI={10.1016/j.ejor.2005.04.025}, abstractNote={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.}, number={3}, journal={EUROPEAN JOURNAL OF OPERATIONAL RESEARCH}, author={Lada, Emily K. and Wilson, James R.}, year={2006}, month={Nov}, pages={1769–1801} }
@article{lada_steiger_wilson_2006, title={Performance evaluation of recent procedures for steady-state simulation analysis}, volume={38}, ISSN={["1545-8830"]}, DOI={10.1080/07408170600735520}, abstractNote={The performance of the batch-means procedure ASAP3 and the spectral procedure WASSP is evaluated on test problems with characteristics typical of practical applications of steady-state simulation analysis procedures. ASAP3 and WASSP are sequential procedures designed to produce a confidence-interval estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. ASAP3 is based on an inverse Cornish-Fisher expansion for the classical batch-means t-ratio, whereas WASSP is based on a wavelet estimator of the batch-means power spectrum. Regarding closeness of the empirical coverage probability and average half-length of the delivered confidence intervals to their respective nominal levels, both procedures compared favorably with the Law-Carson procedure and the original ASAP algorithm. Regarding the average sample sizes required for decreasing levels of maximum confidence-interval half-length, ASAP3 and WASSP exhibited reasonable efficiency in the test problems.}, number={9}, journal={IIE TRANSACTIONS}, author={Lada, Emily K. and Steiger, Natalie M. and Wilson, James R.}, year={2006}, month={Sep}, pages={711–727} }
@article{irizarry_kuhl_lada_subramanian_wilson_2003, title={Analyzing transformation-based simulation metamodels}, volume={35}, DOI={10.1080/07408170390175495}, number={3}, journal={IIE Transactions}, author={Irizarry, M. D. A. and Kuhl, M. E. and Lada, E. K. and Subramanian, S. and Wilson, J. R.}, year={2003}, pages={271–283} }
@article{lada_lu_wilson_2002, title={A wavelet-based procedure for process fault detection}, volume={15}, ISSN={["0894-6507"]}, DOI={10.1109/66.983447}, abstractNote={To detect faults in a time-dependent process, we apply a discrete wavelet transform (DWT) to several independently replicated data sets generated by that process. The DWT can capture irregular data patterns such as sharp "jumps" better than the Fourier transform and standard statistical procedures without adding much computational complexity. Our wavelet coefficient selection method effectively balances model parsimony against data reconstruction error. The few selected wavelet coefficients serve as the "reduced-size" data set to facilitate an efficient decision-making method in situations with potentially large-volume data sets. We develop a general procedure to detect process faults based on differences between the reduced-size data sets obtained from the nominal (in-control) process and from a new instance of the target process that must be tested for an out-of-control condition. The distribution of the test statistic is constructed first using normal distribution theory and then with a new resampling procedure called "reversed jackknifing" that does not require any restrictive distributional assumptions. A Monte Carlo study demonstrates the effectiveness of these procedures. Our methods successfully detect process faults for quadrupole mass spectrometry samples collected from a rapid thermal chemical vapor deposition process.}, number={1}, journal={IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING}, author={Lada, EK and Lu, JC and Wilson, JR}, year={2002}, month={Feb}, pages={79–90} }