Variance reduction methods for simulation of densities on Wiener space

Welcome to the UPF Digital Repository

SIAM Journal of Numerical Analysis, 12, (2002) pp.423-476
http://hdl.handle.net/10230/946
To cite or link this document: http://hdl.handle.net/10230/946
dc.contributor.author Kohatsu, Arturo
dc.contributor.author Pettersson, Roger
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.issued 2002-01-01
dc.identifier.citation SIAM Journal of Numerical Analysis, 12, (2002) pp.423-476
dc.identifier.uri http://hdl.handle.net/10230/946
dc.description.abstract We develop a general error analysis framework for the Monte Carlo simulation of densities for functionals in Wiener space. We also study variance reduction methods with the help of Malliavin derivatives. For this, we give some general heuristic principles which are applied to diffusion processes. A comparison with kernel density estimates is made.
dc.language.iso eng
dc.relation.ispartofseries Economics and Business Working Papers Series; 597
dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Variance reduction methods for simulation of densities on Wiener space
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2014-06-03T07:14:06Z
dc.subject.keyword Statistics, Econometrics and Quantitative Methods
dc.subject.keyword stochastic differential equations
dc.subject.keyword weak approximation
dc.subject.keyword variance reduction
dc.subject.keyword kernel density estimation
dc.rights.accessRights info:eu-repo/semantics/openAccess


See full text
This document is licensed under a Creative Commons license:

Search


Advanced Search

Browse

My Account

Statistics