@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{tafazzoli_roberts_klein_ness_dittus_2009, title={Probabilistic Cost-Effectiveness Comparison of Screening Strategies for Colorectal Cancer}, volume={19}, ISSN={["1558-1195"]}, DOI={10.1145/1502787.1502789}, abstractNote={A stochastic discrete-event simulation model of the natural history of Colorectal Cancer (CRC) is augmented with screening technology representations to create a base for simulating various screening strategies for CRC. The CRC screening strategies recommended by the American Gastroenterological Association (AGA) and the newest screening strategies for which clinical efficacy has been established are simulated. In addition to verification steps, validation of screening is pursued by comparison with the Minnesota Colon Cancer Control Study. The model accumulates discounted costs and quality-adjusted life-years. The natural variability in the modeled random variables for natural history is conditioned using a probabilistic sensitivity analysis through a two-stage sampling process that adds other random variables representing parametric uncertainty. The analysis of the screening alternatives in a low-risk population explores both deterministic and stochastic dominance to eliminate some screening alternatives. Net benefit analysis, based on willingness to pay for quality-adjusted life-years, is used to compare the most cost-effective strategies through acceptability curves and to make a screening recommendation. Methodologically, this work demonstrates how variability from the natural variation in the development, screening, and treatment of a disease can be combined with the variation in parameter uncertainty. Furthermore, a net benefit analysis that characterizes cost-effectiveness alternatives can explicitly depend on variation from all sources producing a probabilistic cost-effectiveness analysis of decision alternatives.}, number={2}, journal={ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION}, author={Tafazzoli, Ali and Roberts, Stephen and Klein, Robert and Ness, Reid and Dittus, Robert}, year={2009}, month={Mar} }
 @article{tafazzoli_wilson_lada_steiger_2008, title={SKART: A SKEWNESS- AND AUTOREGRESSION-ADJUSTED BATCH-MEANS PROCEDURE FOR SIMULATION ANALYSIS}, ISBN={["978-1-4244-2707-9"]}, DOI={10.1109/wsc.2008.4736092}, abstractNote={We discuss Skart, an automated batch-means procedure for constructing a skewness- and autoregression-adjusted confidence interval for the steady-state mean of a simulation output process. Skart is a sequential procedure designed to deliver a confidence interval that satisfies user-specified requirements concerning not only coverage probability but also the absolute or relative precision provided by the half-length. Skart exploits separate adjustments to the half-length of the classical batch-means confidence interval so as to account for the effects on the distribution of the underlying Student's t -statistic that arise from 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 simulation analysis methods-namely, its predecessors ASAP3, WASSP, and SBatch as well as ABATCH, LBATCH, the Heidelberger-Welch procedure, and the Law-Carson procedure.}, journal={2008 WINTER SIMULATION CONFERENCE, VOLS 1-5}, author={Tafazzoli, Ali and Wilson, James R. and Lada, Emily K. and Steiger, Natalie M.}, year={2008}, pages={387-+} }