{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T19:09:54Z","timestamp":1672427394265},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2006,7]]},"abstract":"We develop a class of techniques for analyzing the output of simulations of a semi-regenerative process. Called the semi-regenerative method, the approach is a generalization of the regenerative method, and it can increase efficiency. We consider the estimation of various performance measures, including steady-state means, expected cumulative reward until hitting a set of states, derivatives of steady-state means, and time-average variance constants. We also discuss importance sampling and a bias-reduction technique. In each case, we develop two estimators: one based on a simulation of a single sample path, and the other a type of stratified estimator in which trajectories are generated in an independent and identically distributed manner. We establish a central limit theorem for each estimator so confidence intervals can be constructed.<\/jats:p>","DOI":"10.1145\/1147224.1147228","type":"journal-article","created":{"date-parts":[[2006,10,18]],"date-time":"2006-10-18T18:11:32Z","timestamp":1161195092000},"page":"280-315","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["The semi-regenerative method of simulation output analysis"],"prefix":"10.1145","volume":"16","author":[{"given":"James M.","family":"Calvin","sequence":"first","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ"}]},{"given":"Peter W.","family":"Glynn","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Marvin K.","family":"Nakayama","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ"}]}],"member":"320","published-online":{"date-parts":[[2006,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 1998 Winter Simulation Conference, D. 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IEEE, Los Alamitos, CA. 1479--1486."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1017\/S0269964800004022","article-title":"Accelerated regeneration for Markov chain simulations","volume":"9","author":"Andrad\u00f6ttir S.","year":"1995","unstructured":"Andrad\u00f6ttir , S. , Calvin , J. M. , and Glynn , P. W. 1995 . Accelerated regeneration for Markov chain simulations . Probab. Engin. Informat. Sciences 9 , 497 -- 523 . Andrad\u00f6ttir, S., Calvin, J. M., and Glynn, P. W. 1995. Accelerated regeneration for Markov chain simulations. Probab. Engin. Informat. Sciences 9, 497--523.","journal-title":"Probab. Engin. Informat. Sciences"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1214\/aoms\/1177705136","article-title":"Statistical methods in Markov chains","volume":"32","author":"Billingsley P.","year":"1961","unstructured":"Billingsley , P. 1961 . Statistical methods in Markov chains . Ann. Mathemat. 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