{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:19:39Z","timestamp":1750306779462,"version":"3.41.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2013,7,1]],"date-time":"2013-07-01T00:00:00Z","timestamp":1372636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>\n            Consider a simulation estimator\n            <jats:italic>\u03b1<\/jats:italic>\n            (\n            <jats:italic>c<\/jats:italic>\n            ) based on expending\n            <jats:italic>c<\/jats:italic>\n            units of computer time to estimate a quantity\n            <jats:italic>\u03b1<\/jats:italic>\n            . In comparing competing estimators for\n            <jats:italic>\u03b1<\/jats:italic>\n            , a natural figure of merit is to choose the estimator that minimizes the computation time needed to reduce the error probability\n            <jats:italic>P<\/jats:italic>\n            (|\n            <jats:italic>\u03b1<\/jats:italic>\n            (\n            <jats:italic>c<\/jats:italic>\n            )\u2009\u2212\u2009\n            <jats:italic>\u03b1<\/jats:italic>\n            |\u2009&gt;\u2009\n            <jats:italic>\u03b5<\/jats:italic>\n            ) to below some prescribed value\n            <jats:italic>\u03b4<\/jats:italic>\n            . In this paper, we develop large deviations results that provide approximations to the computational budget necessary to reduce the error probability to below\n            <jats:italic>\u03b4<\/jats:italic>\n            when\n            <jats:italic>\u03b4<\/jats:italic>\n            is small. This approximation depends critically on both the distribution of the estimator itself and that of the random amount of computer time required to generate the estimator, and leads to different conclusions regarding the choice of preferred estimator than those obtained when one requires the error tolerance\n            <jats:italic>\u03b5<\/jats:italic>\n            to be small. The \u201csmall\n            <jats:italic>\u03b5<\/jats:italic>\n            \u201d regime leads to variance-based selection criteria, and has a long history in the simulation literature going back to Hammersley and Handscomb.\n          <\/jats:p>","DOI":"10.1145\/2499913.2499919","type":"journal-article","created":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T15:14:00Z","timestamp":1375370040000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Asymptotic Simulation Efficiency Based on Large Deviations"],"prefix":"10.1145","volume":"23","author":[{"given":"Peter W.","family":"Glynn","sequence":"first","affiliation":[{"name":"Stanford University"}]},{"given":"Sandeep","family":"Juneja","sequence":"additional","affiliation":[{"name":"Tata Institute of Fundamental Research"}]}],"member":"320","published-online":{"date-parts":[[2013,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008349927281"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/105051607000000023"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022651401451"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Dembo A. and Zeitouni O. 1998. Large Deviations Techniques and Applications 2nd Ed. Springer New York.  Dembo A. and Zeitouni O. 1998. Large Deviations Techniques and Applications 2nd Ed. Springer New York.","DOI":"10.1007\/978-1-4612-5320-4"},{"volume-title":"Proceedings of the Winter Simulation Conference. IEEE, 577--585","author":"Glynn P. W.","key":"e_1_2_1_5_1","unstructured":"Glynn , P. W. and Juneja , S . 2004. A large deviations perspective on ordinal optimization . In Proceedings of the Winter Simulation Conference. IEEE, 577--585 . Glynn, P. W. and Juneja, S. 2004. A large deviations perspective on ordinal optimization. In Proceedings of the Winter Simulation Conference. IEEE, 577--585."},{"volume-title":"Proceedings of the Winter Simulation Conference. IEEE, 396--406","author":"Glynn P. W.","key":"e_1_2_1_6_1","unstructured":"Glynn , P. W. and Juneja , S . 2008. A large deviations view of asymptotic efficiency for simulation estimators . In Proceedings of the Winter Simulation Conference. IEEE, 396--406 . Glynn, P. W. and Juneja, S. 2008. A large deviations view of asymptotic efficiency for simulation estimators. In Proceedings of the Winter Simulation Conference. IEEE, 396--406."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.40.3.505"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Hammersley J. M. and Handscomb D. C. 1964. Monte Carlo Methods. Methuen London.  Hammersley J. M. and Handscomb D. C. 1964. Monte Carlo Methods. Methuen London.","DOI":"10.1007\/978-94-009-5819-7"},{"volume-title":"Proceedings of the Winter Simulation Conference. IEEE Press, 995--1002","author":"Hunter R. S.","key":"e_1_2_1_9_1","unstructured":"Hunter , R. S. and Pasupathy , R . 2012. Large-deviation sampling laws for constrained simulation optimization on finite sets . In Proceedings of the Winter Simulation Conference. IEEE Press, 995--1002 . Hunter, R. S. and Pasupathy, R. 2012. Large-deviation sampling laws for constrained simulation optimization on finite sets. In Proceedings of the Winter Simulation Conference. IEEE Press, 995--1002."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-8858(85)90017-X"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(90)90060-6"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01210324"},{"volume-title":"Proceedings of the Winter Simulation Conference. IEEE, 273--280","author":"Szechtman R.","key":"e_1_2_1_13_1","unstructured":"Szechtman , R. and Yucesan , E . 2008. A new perspective on feasibility determination . In Proceedings of the Winter Simulation Conference. IEEE, 273--280 . Szechtman, R. and Yucesan, E. 2008. A new perspective on feasibility determination. In Proceedings of the Winter Simulation Conference. IEEE, 273--280."}],"container-title":["ACM Transactions on Modeling and Computer Simulation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2499913.2499919","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2499913.2499919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:34:00Z","timestamp":1750232040000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2499913.2499919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7]]},"references-count":13,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2013,7]]}},"alternative-id":["10.1145\/2499913.2499919"],"URL":"https:\/\/doi.org\/10.1145\/2499913.2499919","relation":{},"ISSN":["1049-3301","1558-1195"],"issn-type":[{"type":"print","value":"1049-3301"},{"type":"electronic","value":"1558-1195"}],"subject":[],"published":{"date-parts":[[2013,7]]},"assertion":[{"value":"2009-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-07-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}