{"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":1750306779089,"version":"3.41.0"},"reference-count":20,"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>We consider optimization of expected system performance by random search. There are two sources of random variation in this process: (i) a search-induced variability because the expected performance of the system will vary randomly according to the alternatives randomly selected for examination, and (ii) a simulation induced variability, because there will be random error in estimating expected system performance from finite simulation runs. We show that, in altering the balance between these two sources of variability, three distinct forms of asymptotic behavior of the estimate of the optimal expected system performance are possible. The form of the asymptotic results shows that they may be not be easy to apply in practical work. As an alternative, a methodology for fitting a statistical model that accounts for both types of variability is suggested. This then allows the distributional properties of quantities of interest, like the optimum performance value and the best value obtained by the search, to be estimated by resampling and which also allows a test of goodness of fit of the model. Four numerical examples are given.<\/jats:p>","DOI":"10.1145\/2499913.2499914","type":"journal-article","created":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T15:14:00Z","timestamp":1375370040000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Fitting Statistical Models of Random Search in Simulation Studies"],"prefix":"10.1145","volume":"23","author":[{"given":"Russell C. H.","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Mathematics, University of Southampton U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,7]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729437"},{"key":"e_1_2_2_2_1","unstructured":"Archetti F. Betr\u00f2 B. and Steff\u00e8 S. 1977. A theoretical framework for global optimization via random sampling. Tech. rep. Cuaderni del Dipartimento di Ricerca Operative e Scienze Statische Universit\u00e0 di Pisa. Archetti F. Betr\u00f2 B. and Steff\u00e8 S. 1977. A theoretical framework for global optimization via random sampling. Tech. rep. Cuaderni del Dipartimento di Ricerca Operative e Scienze Statische Universit\u00e0 di Pisa."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoap\/1177005712"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345637"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1988.10478718"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jos.4250009"},{"key":"e_1_2_2_7_1","volume-title":"Proceedings of the INFORMS Simulation Society Workshop. C.-H","author":"Cheng R. C. H.","year":"2007","unstructured":"Cheng , R. C. H. 2007. DES as a real time decision making tool. An application to fire service emergency cover . In Proceedings of the INFORMS Simulation Society Workshop. C.-H . Chen and S. G. Henderson Eds., http:\/\/www.informs.org\/Community\/Simulation-Society\/Other-Conferences-and-Workshops\/Society-Research-Workshop2\/ 2007 -Simulation-Workshop, pdf #17. Cheng, R. C. H. 2007. DES as a real time decision making tool. An application to fire service emergency cover. In Proceedings of the INFORMS Simulation Society Workshop. C.-H. Chen and S. G. Henderson Eds., http:\/\/www.informs.org\/Community\/Simulation-Society\/Other-Conferences-and-Workshops\/Society-Research-Workshop2\/2007-Simulation-Workshop, pdf #17."},{"key":"e_1_2_2_8_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1111\/j.2517-6161.1983.tb01268.x","article-title":"Estimating parameters in continuous univariate distributions with a shifted origin","volume":"45","author":"Cheng R. C. H.","year":"1983","unstructured":"Cheng , R. C. H. and Amin , N. A. K. 1983 . Estimating parameters in continuous univariate distributions with a shifted origin . J. Roy. Stat. Soc. Ser. B , 45 , 394 -- 403 . Cheng, R. C. H. and Amin, N. A. K. 1983. Estimating parameters in continuous univariate distributions with a shifted origin. J. Roy. Stat. Soc. Ser. B, 45, 394--403.","journal-title":"J. Roy. Stat. Soc. Ser. B"},{"key":"e_1_2_2_10_1","unstructured":"Chia Y. L. and Glynn P. W. 2007. Optimal convergence rate for random search. In Proceedings of the INFORMS Simulation Society Workshop. C.-H. Chen and S. G. Henderson Eds. http:\/\/www.informs.org\/Community\/Simulation-Society\/Other-Conferences-and-Workshops\/Society-Research-Workshop2\/2007-Simulation-Workshop pdf #11. Chia Y. L. and Glynn P. W. 2007. Optimal convergence rate for random search. In Proceedings of the INFORMS Simulation Society Workshop. C.-H. Chen and S. G. Henderson Eds. http:\/\/www.informs.org\/Community\/Simulation-Society\/Other-Conferences-and-Workshops\/Society-Research-Workshop2\/2007-Simulation-Workshop pdf #11."},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Davison A. C. and Hinkley D. V. 1997. Bootstrap Methods and Their Application. Cambridge University Press Cambridge UK. Davison A. C. and Hinkley D. V. 1997. Bootstrap Methods and Their Application. 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The maximum spacing method: An estimation method related to the maximum likelihood method . Scand. J. Statist. 11 , 93 -- 112 . Ranneby, B. 1984. The maximum spacing method: An estimation method related to the maximum likelihood method. Scand. J. Statist. 11, 93--112.","journal-title":"Scand. J. Statist."},{"key":"e_1_2_2_18_1","series-title":"Lecture Notes in Mathematics","volume-title":"Regularly varying functions","author":"Seneta E.","unstructured":"Seneta , E. 1976. Regularly varying functions . Lecture Notes in Mathematics , vol. 508 . Springer-Verlag , Berlin . Seneta, E. 1976. Regularly varying functions. Lecture Notes in Mathematics, vol. 508. 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