{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:01:33Z","timestamp":1742990493671,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030983185"},{"type":"electronic","value":"9783030983192"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-98319-2_1","type":"book-chapter","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T07:03:13Z","timestamp":1653030193000},"page":"3-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Density Estimation by Monte Carlo and Quasi-Monte Carlo"],"prefix":"10.1007","author":[{"given":"Pierre","family":"L\u2019Ecuyer","sequence":"first","affiliation":[]},{"given":"Florian","family":"Puchhammer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Asmussen, S.: Conditional Monte Carlo for sums, with applications to insurance and finance. Ann. Actuar. Sci. 12(2), 455\u2013478 (2018)","DOI":"10.1017\/S1748499517000252"},{"key":"1_CR2","unstructured":"Asmussen, S., Glynn, P.W.: Stochastic Simulation. Springer, New York (2007)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Ben Abdellah, A., L\u2019Ecuyer, P., Owen, A., Puchhammer, F.: Density estimation by randomized Quasi-Monte Carlo. SIAM J. Uncertain. Quantif. 9(1), 280\u2013301 (2021)","DOI":"10.1137\/19M1259213"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Dick, J., Pillichshammer, F.: Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration. Cambridge University Press, Cambridge, U.K. (2010)","DOI":"10.1017\/CBO9780511761188"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Fu, M., Hu, J.Q.: Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Kluwer Academic, Boston (1997)","DOI":"10.1007\/978-1-4615-6293-1"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Fu, M.C.: Sensitivity analysis in Monte Carlo simulation of stochastic activity networks. In: Alt, F.B., Fu, M.C., Golden, B.L. (eds.) Perspectives in Operations Research, Operations Research\/Computer Science Interfaces Series, pp. 351\u2013366. Springer, Boston (2006)","DOI":"10.1007\/978-0-387-39934-8_20"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Glynn, P.W.: Likelihood ratio gradient estimation: an overview. In: Proceedings of the 1987 Winter Simulation Conference, pp. 366\u2013375. IEEE Press, Piscataway, NJ (1987)","DOI":"10.21236\/ADA197085"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Glynn, P.W., L\u2019Ecuyer, P.: Likelihood ratio gradient estimation for regenerative stochastic recursions. Adv. Appl. Probab. 27, 1019\u20131053 (1995)","DOI":"10.1017\/S0001867800047789"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Goda, T., Suzuki, K.: Recent Advances in Higher Order Quasi-Monte Carlo Methods, pp. 69\u2013102. De Gruyter (2019)","DOI":"10.1515\/9783110652581-004"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Laub, P.J., Salomone, R., Botev, Z.I.: Monte Carlo estimation of the density of the sum of dependent random variables. Mathematics and Computers in Simulation 161, 23\u201331 (2019)","DOI":"10.1016\/j.matcom.2018.12.001"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P.: A unified view of the IPA, SF, and LR gradient estimation techniques. Manag. Sci. 36(11), 1364\u20131383 (1990)","DOI":"10.1287\/mnsc.36.11.1364"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P.: Quasi-Monte Carlo methods with applications in finance. Financ. Stoch. 13(3), 307\u2013349 (2009)","DOI":"10.1007\/s00780-009-0095-y"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P.: Randomized Quasi-Monte Carlo: an introduction for practitioners. In: Glynn, P.W., Owen, A.B. (eds.) Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2016, pp. 29\u201352. Springer, Berlin (2018)","DOI":"10.1007\/978-3-319-91436-7_2"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P., Munger, D.: Algorithm 958: lattice builder: a general software tool for constructing rank-1 lattice rules. ACM Trans. Math. Softw. 42(2), Article 15 (2016)","DOI":"10.1145\/2754929"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P., Perron, G.: On the convergence rates of IPA and FDC derivative estimators. Oper. Res. 42(4), 643\u2013656 (1994)","DOI":"10.1287\/opre.42.4.643"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Puchhammer, F., L\u2019Ecuyer, P.: Likelihood Ratio Density Estimation for Simulation Models (2022). Submitted","DOI":"10.1109\/WSC57314.2022.10015497"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"L\u2019Ecuyer, P., Puchhammer, F., Ben Abdellah, A.: Monte Carlo and Quasi-Monte Carlo density estimation via conditioning. INFORMS J. Comput. (2022). To appear. See https:\/\/doi.org\/10.1287\/ijoc.2021.1135","DOI":"10.1287\/ijoc.2021.1135"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Lei, L., Peng, Y., Fu, M.C., Hu, J.Q.: Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation. Discret. Event Dyn. Syst. 28(1), 109\u2013125 (2018)","DOI":"10.1007\/s10626-017-0247-8"},{"key":"1_CR19","unstructured":"Lemieux, C., Cieslak, M., Luttmer, K.: RandQMC User\u2019s Guide: A Package for Randomized Quasi-Monte Carlo Methods in C (2004). Software user\u2019s guide, available at http:\/\/www.math.uwaterloo.ca\/~clemieux\/randqmc.html"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Niederreiter, H.: Random number generation and Quasi-Monte Carlo methods. In: SIAM CBMS-NSF CBMS Regional Conference Series in Mathematics, vol.63. SIAM (1992)","DOI":"10.1137\/1.9781611970081"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065\u20131076 (1962)","DOI":"10.1214\/aoms\/1177704472"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Peng, Y., Fu, M.C., Heidergott, B., Lam, H.: Maximum likelihood estimation by Monte Carlo simulation: towards data-driven stochastic modeling. Oper. Res. 68(6), 1896\u20131912 (2020)","DOI":"10.1287\/opre.2019.1978"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Peng, Y., Fu, M.C., Hu, J.Q., Heidergott, B.: A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters. Oper. Res. 66(2), 487\u2013499 (2018)","DOI":"10.1287\/opre.2017.1674"},{"key":"1_CR24","unstructured":"Peng, Y., Fu, M.C., Hu, J.Q., L\u2019Ecuyer, P., Tuffin, B.: Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs (2021). Submitted manuscript"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Peng, Y., Fu, M.C., Hu, J.Q., L\u2019Ecuyer, P., Tuffin, B.: Variance reduction for generalized likelihood ratio method by conditional Monte Carlo and randomized Quasi-Monte Carlo. J. Manage. Sci. Eng. (2022). To appear","DOI":"10.1016\/j.jmse.2022.02.002"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Scott, D.W.: Multivariate Density Estimation, 2nd edn. Wiley, New York (2015)","DOI":"10.1002\/9781118575574"}],"container-title":["Springer Proceedings in Mathematics & Statistics","Monte Carlo and Quasi-Monte Carlo Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98319-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T18:41:08Z","timestamp":1675622468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98319-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030983185","9783030983192"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98319-2_1","relation":{},"ISSN":["2194-1009","2194-1017"],"issn-type":[{"type":"print","value":"2194-1009"},{"type":"electronic","value":"2194-1017"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCQMC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcqmc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mcqmc20.web.ox.ac.uk\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}