dc.contributor.author L'Ecuyer, P. dc.contributor.author Puchhammer, F. dc.contributor.author Ben Abdellah, A. dc.date.accessioned 2021-09-07T16:08:05Z dc.date.available 2021-09-07T16:08:05Z dc.date.issued 2021 dc.identifier.uri http://hdl.handle.net/20.500.11824/1327 dc.description.abstract Estimating the unknown density from which a given independent sample originates is more difficult than estimating the mean, in the sense that for the best popular non-parametric density estimators, the mean integrated square error converges more slowly than at the canonical rate of $\mathcal{O}(1/n)$. When the sample is generated from a simulation model and we have control over how this is done, we can do better. We examine an approach in which conditional Monte Carlo yields, under certain conditions, a random conditional density which is an unbiased estimator of the true density at any point. By averaging independent replications, we obtain a density estimator that converges at a faster rate than the usual ones. Moreover, combining this new type of estimator with randomized quasi-Monte Carlo to generate the samples typically brings a larger improvement on the error and convergence rate than for the usual estimators, because the new estimator is smoother as a function of the underlying uniform random numbers. en_US dc.description.sponsorship IVADO Research Grant, NSERC-Canada Discorvery Grant, Canada Research Chair, Inria International Chair, ERDF, ESF, EXP. 2019/00432 en_US dc.format application/pdf en_US dc.language.iso eng en_US dc.rights Reconocimiento-NoComercial-CompartirIgual 3.0 España en_US dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/es/ en_US dc.subject density estimation en_US dc.subject conditional Monte Carlo en_US dc.subject quasi-Monte Carlo en_US dc.title Monte Carlo and Quasi-Monte Carlo Density Estimation via Conditioning en_US dc.type info:eu-repo/semantics/article en_US dc.relation.projectID ES/1PE/SEV-2017-0718 en_US dc.relation.projectID ES/2PE/PID2019-104927GB-C22 en_US dc.relation.projectID ES/2PE/PID2019-108111RB-I00 en_US dc.relation.projectID EUS/BERC/BERC.2018-2021 en_US dc.relation.projectID EUS/ELKARTEK en_US dc.rights.accessRights info:eu-repo/semantics/openAccess en_US dc.type.hasVersion info:eu-repo/semantics/acceptedVersion en_US dc.journal.title INFORMS Journal on Computing en_US
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