{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:41:20Z","timestamp":1777902080482,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2003,10,1]],"date-time":"2003-10-01T00:00:00Z","timestamp":1064966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2003,10]]},"abstract":"<jats:p>This article discusses the implementation of using finite differences to construct a confidence interval for a simulation estimator of the derivative of the steady-state distribution of a stochastic process. The quasi-independent procedure increases the simulation run length progressively until a certain number of essentially independent and identically distributed systematic samples are obtained. The author computes sample quantiles at certain grid points and constructs a histogram from those grid points. The derivative estimate is then computed from the histogram (i.e., the empirical distribution). An experimental performance evaluation demonstrates the validity of using this procedure to estimate the derivatives.<\/jats:p>","DOI":"10.1177\/0037549703039951","type":"journal-article","created":{"date-parts":[[2004,4,21]],"date-time":"2004-04-21T20:41:37Z","timestamp":1082580097000},"page":"598-609","source":"Crossref","is-referenced-by-count":4,"title":["Derivative Estimation with Finite Differences"],"prefix":"10.1177","volume":"79","author":[{"given":"E. Jack","family":"Chen","sequence":"first","affiliation":[{"name":"BASF Corporation 3000 Continental Drive\u2013North Mount Olive, NJ                        07828, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2003,10,1]]},"reference":[{"key":"atypb1","unstructured":"[1] Law, A. M., and W. D. Kelton. 2000. Simulation modeling and analysis. 3d ed. New York: McGraw-Hill ."},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"[2] L\u2019Ecuyer, P. 1991. An overview of derivative estimation. In Proceedings of the 1991 Winter Simulation Conference, edited by B. L. Nelson, W. D. Kelton, and G. M. Clark, 207-217. Piscataway, NJ: Institute of Electrical and Electronics Engineers .","DOI":"10.1109\/WSC.1991.185617"},{"key":"atypb3","doi-asserted-by":"crossref","unstructured":"[3] Fu, M. C., and J. Q. Hu. 1997. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer .","DOI":"10.1007\/978-1-4615-6293-1"},{"key":"atypb4","unstructured":"[4] Rubinstein, R. Y., and A. Shapiro. 1993. Discrete event systems: Sensitivity analysis and stochastic optimization by the score function method. New York: John Wiley ."},{"key":"atypb5","doi-asserted-by":"crossref","unstructured":"[5] Pflug, G. C. 1996. Optimization of stochastic models: The interface between simulation and optimization. Boston: Kluwer .","DOI":"10.1007\/978-1-4613-1449-3"},{"key":"atypb6","doi-asserted-by":"crossref","unstructured":"[6] Glynn, P. W. 1989. Optimization of stochastic systems via simulation . In Proceedings of the 1989 Winter Simulation Conference, pp. 90-105 .","DOI":"10.1109\/WSC.1989.718667"},{"key":"atypb7","doi-asserted-by":"crossref","unstructured":"[7] Kushner, H. J., and D. S. Clark. 1978. Stochastic approximation methods for constrained and unconstrained systems. New York: Springer-Verlag .","DOI":"10.1007\/978-1-4684-9352-8"},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1287\/opre.42.4.643"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1007\/BF02136830"},{"key":"atypb10","doi-asserted-by":"crossref","unstructured":"[10] Chen, E. J., and W. D. Kelton. 2001. Quantile and histogram estimation. In Proceedings of the 2001 Winter Simulation Conference, edited by B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, 451-459. Piscataway, NJ: Institute of Electrical and Electronics Engineers .","DOI":"10.1109\/WSC.2001.977322"},{"key":"atypb11","doi-asserted-by":"publisher","DOI":"10.1177\/003754970107600504"},{"key":"atypb12","doi-asserted-by":"crossref","unstructured":"[12] Goldsman, D., and B. W. Schmeiser. 1997. Computational efficiency of batching methods. In Proceedings of the 1997 Winter Simulation Conference, edited by S. Andrad\u00f3ttir, K. J. Healy, D. H. Withers, and B. L. Nelson, 202-207. Piscataway, NJ: Institute of Electrical and Electronics Engineers .","DOI":"10.1145\/268437.268479"},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1016\/0047-259X(72)90011-5"},{"key":"atypb14","unstructured":"[14] Knuth, D. E. 1998. The art of computer programming. Vol. 2, 3d ed. Reading, MA: Addison-Wesley ."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1016\/S1569-190X(03)00048-0"}],"container-title":["SIMULATION"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549703039951","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549703039951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:18:09Z","timestamp":1777634289000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0037549703039951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,10]]},"references-count":15,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2003,10]]}},"alternative-id":["10.1177\/0037549703039951"],"URL":"https:\/\/doi.org\/10.1177\/0037549703039951","relation":{},"ISSN":["0037-5497","1741-3133"],"issn-type":[{"value":"0037-5497","type":"print"},{"value":"1741-3133","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,10]]}}}