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Simul."],"published-print":{"date-parts":[[2006,1]]},"abstract":"<jats:p>\n            Ranking and selection procedures (R&amp;S) were developed by statisticians to search for the best among a small collection of populations or treatments, where the \u201cbest\u201d treatment is typically the one with the largest or smallest expected(long-run average) response. R&amp;S procedures have been successfully extended to address situations that are encountered in stochastic simulation of alternative system designs, including unequal variances across alternatives, dependence both within the output of each system and across the outputs from alternative systems, and large numbers of alternatives to compare. In nearly all cases the estimator of the expected response is a (perhaps generalized) sample mean of the output of interest. In this article we derive R&amp;S procedures that employ\n            <jats:italic>control-variate estimators<\/jats:italic>\n            instead of sample means. Control variates can be much more statistically efficient than sample means, leading to R&amp;S procedures that are correspondingly more efficient. We also consider the related problem of estimating the expected value of the best (as opposed to the selected) system design.\n          <\/jats:p>","DOI":"10.1145\/1122012.1122015","type":"journal-article","created":{"date-parts":[[2006,5,8]],"date-time":"2006-05-08T16:09:20Z","timestamp":1147104560000},"page":"52-75","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Control variates for screening, selection, and estimation of the best"],"prefix":"10.1145","volume":"16","author":[{"given":"Barry L.","family":"Nelson","sequence":"first","affiliation":[{"name":"Northwestern University, Evanston, IL"}]},{"given":"Jeremy","family":"Staum","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, IL"}]}],"member":"320","published-online":{"date-parts":[[2006,1]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"An Introduction to Multivariate Statistical Analysis","author":"Anderson T. 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Design and Analysis of Experiments for Statistical Selection Screening and Multiple Comparisons. John Wiley & Sons New York."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.51.5.814.16751"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1080\/01621459.1976.10481561","article-title":"Procedures for fixed-width interval estimation of the largest normal mean","volume":"71","author":"Chen H. J.","year":"1976","unstructured":"Chen , H. J. and Dudewicz , E. J. 1976 . Procedures for fixed-width interval estimation of the largest normal mean . J. Amer. Statis. Assoc. 71 , 752 -- 756 . Chen, H. J. and Dudewicz, E. J. 1976. Procedures for fixed-width interval estimation of the largest normal mean. J. Amer. Statis. Assoc. 71, 752--756.","journal-title":"J. Amer. Statis. Assoc."},{"key":"e_1_2_1_6_1","volume-title":"Monte Carlo Methods in Financial Engineering","author":"Glasserman P.","unstructured":"Glasserman , P. 2004. Monte Carlo Methods in Financial Engineering . Springer-Verlag , New York . Glasserman, P. 2004. Monte Carlo Methods in Financial Engineering. Springer-Verlag, New York."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1080\/03610918508812467","article-title":"A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation","volume":"14","author":"Koenig L. W.","year":"1985","unstructured":"Koenig , L. W. and Law , A. M. 1985 . A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation . Communications in Statistics B14 , 719 -- 734 . Koenig, L. W. and Law, A. M. 1985. A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation. Communications in Statistics B14, 719--734.","journal-title":"Communications in Statistics"},{"key":"e_1_2_1_8_1","first-page":"322","article-title":"A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Manage","volume":"27","author":"Lavenberg S. S.","year":"1981","unstructured":"Lavenberg , S. S. and Welch , P. D. 1981 . A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Manage . Sci. 27 , 322 -- 335 . Lavenberg, S. S. and Welch, P. D. 1981. A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Manage. Sci. 27, 322--335.","journal-title":"Sci."},{"key":"e_1_2_1_9_1","unstructured":"Law A. M. and Kelton W. D. 2000. Simulation Modeling and Analysis 3rd ed. McGraw-Hill New York.   Law A. M. and Kelton W. D. 2000. Simulation Modeling and Analysis 3rd ed. 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