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dc.contributor.authorL'Ecuyer, P.
dc.contributor.authorPuchhammer, F. 
dc.contributor.authorBen Abdellah, A.
dc.date.accessioned2021-09-07T16:08:05Z
dc.date.available2021-09-07T16:08:05Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1327
dc.description.abstractEstimating 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.sponsorshipIVADO Research Grant, NSERC-Canada Discorvery Grant, Canada Research Chair, Inria International Chair, ERDF, ESF, EXP. 2019/00432en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectdensity estimationen_US
dc.subjectconditional Monte Carloen_US
dc.subjectquasi-Monte Carloen_US
dc.titleMonte Carlo and Quasi-Monte Carlo Density Estimation via Conditioningen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/2PE/PID2019-104927GB-C22en_US
dc.relation.projectIDES/2PE/PID2019-108111RB-I00en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleINFORMS Journal on Computingen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España