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ASAP3 is a sequential procedure designed to produce a confidence-interval estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means\n            <jats:italic>t<\/jats:italic>\n            -ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted confidence interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered confidence interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.\n          <\/jats:p>","DOI":"10.1145\/1044322.1044325","type":"journal-article","created":{"date-parts":[[2005,1,26]],"date-time":"2005-01-26T16:35:53Z","timestamp":1106757353000},"page":"39-73","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":62,"title":["ASAP3: a batch means procedure for steady-state simulation analysis"],"prefix":"10.1145","volume":"15","author":[{"given":"Natalie M.","family":"Steiger","sequence":"first","affiliation":[{"name":"University of Maine, Orona, ME"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily K.","family":"Lada","sequence":"additional","affiliation":[{"name":"Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James R.","family":"Wilson","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey A.","family":"Joines","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Alexopoulos","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Goldsman","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2005,1]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/974734.974738"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Alexopoulos C. and Seila A. F. 1998. Output data analysis. In Handbook of Simulation: Principles Methodology Advances Applications and Practice} J. Banks Ed. John Wiley & Sons New York NY 225--272.  Alexopoulos C. and Seila A. F. 1998. Output data analysis. In Handbook of Simulation: Principles Methodology Advances Applications and Practice} J. Banks Ed. John Wiley & Sons New York NY 225--272.","DOI":"10.1002\/9780470172445.ch7"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/01621459.1972.10481264","article-title":"The effect of aggregation on prediction in the autoregressive model","volume":"67","author":"Amemiya T.","year":"1972","unstructured":"Amemiya , T. and Wu , R. Y. 1972 . The effect of aggregation on prediction in the autoregressive model . J. Amer. Statist. Assoc. 67 , 339 (Sept.), 628--632. Amemiya, T. and Wu, R. Y. 1972. The effect of aggregation on prediction in the autoregressive model. J. Amer. Statist. Assoc. 67, 339 (Sept.), 628--632.","journal-title":"J. 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C. , L\u00f3pez-Garc\u00eda , C. , Rodr\u00edguez-P\u00e9rez , M. , Fern\u00e1ndez-Veiga , M. , and Sousa-Vieira , M. E . 2002. A batch means procedure for mean value estimation of processes exhibiting long range dependence . In Proceedings of the 2002 Winter Simulation Conference, E. Y\u00fccesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, Eds. Institute of Electrical and Electronics Engineers , Piscataway, NJ, 456--464. Available at &lt;http:\/\/www.informs-cs.org\/wsc02papers\/057.pdf&gt;. Su\u00e1rez-Gonz\u00e1lez, A., L\u00f3pez-Ardao, J. C., L\u00f3pez-Garc\u00eda, C., Rodr\u00edguez-P\u00e9rez, M., Fern\u00e1ndez-Veiga, M., and Sousa-Vieira, M. E. 2002. A batch means procedure for mean value estimation of processes exhibiting long range dependence. In Proceedings of the 2002 Winter Simulation Conference, E. Y\u00fccesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, Eds. Institute of Electrical and Electronics Engineers, Piscataway, NJ, 456--464. 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