Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111709
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dc.contributor.authorMilstein, G. N.en
dc.contributor.authorSchoenmakers, J. G. M.en
dc.contributor.authorSpokoiny, V.en
dc.date.accessioned2022-05-12T08:21:05Z-
dc.date.available2022-05-12T08:21:05Z-
dc.date.issued2004-
dc.identifier.citationMilstein G. N. Transition Density Estimation for Stochastic Differential Equations Via Forward-reverse Representations / G. N. Milstein, J. G. M. Schoenmakers, V. Spokoiny // Bernoulli. — 2004. — Vol. 10. — Iss. 2. — P. 281-312.en
dc.identifier.issn1350-7265-
dc.identifier.otherAll Open Access, Bronze, Green3
dc.identifier.urihttp://hdl.handle.net/10995/111709-
dc.description.abstractThe general reverse diffusion equations are derived and applied to the problem of transition density estimation of diffusion processes between two fixed states. For this problem we propose density estimation based on forward-reverse representations and show that this method allows essentially better results to be achieved than the usual kernel or projection estimation based on forward representations only. © 2004 ISI/BS.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherBernoulli Society for Mathematical Statistics and Probabilityen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceBernoulli2
dc.sourceBernoullien
dc.subjectFORWARD AND REVERSE DIFFUSIONen
dc.subjectMONTE CARLO SIMULATIONen
dc.subjectSTATISTICAL ESTIMATIONen
dc.subjectTRANSITION DENSITYen
dc.titleTransition Density Estimation for Stochastic Differential Equations Via Forward-reverse Representationsen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.scopus33644476835-
local.contributor.employeeMilstein, G.N., Weierstrass-Institut für Angewandte Analysis und Stochastik, Berlin, Germany, Ural State University, Ekaterinburg, Russian Federation; Schoenmakers, J.G.M., Weierstrass-Institut für Angewandte Analysis und Stochastik, Berlin, Germany; Spokoiny, V., Weierstrass-Institut für Angewandte Analysis und Stochastik, Berlin, Germanyen
local.description.firstpage281-
local.description.lastpage312-
local.issue2-
local.volume10-
local.contributor.departmentWeierstrass-Institut für Angewandte Analysis und Stochastik, Berlin, Germany; Ural State University, Ekaterinburg, Russian Federationen
local.identifier.eid2-s2.0-33644476835-
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