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Particle filters for partially observed diffusions
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Fearnhead, Paul, Papaspiliopoulos, Omiros and Roberts, Gareth O. (2008) Particle filters for partially observed diffusions. Royal Statistical Society. Journal. Series B: Statistical Methodology, Volume 70 (Number 4). pp. 755-777. doi:10.1111/j.1467-9868.2008.00661.x ISSN 1369-7412.
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Official URL: http://dx.doi.org/10.1111/j.1467-9868.2008.00661.x
Abstract
We introduce a novel particle filter scheme for a class of partially observed multivariate diffusions. We consider a variety of observation schemes, including diffusion observed with error, observation of a subset of the components of the multivariate diffusion and arrival times of a Poisson process whose intensity is a known function of the diffusion (Cox process). Unlike currently available methods, our particle filters do not require approximations of the transition and/or the observation density by using time discretizations. Instead, they build on recent methodology for the exact simulation of the diffusion process and the unbiased estimation of the transition density. We introduce the generalized Poisson estimator, which generalizes the Poisson estimator of Beskos and co-workers. A central limit theorem is given for our particle filter scheme.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Central limit theorem, Filters (Mathematics), Algorithms, Diffusion processes | ||||
Journal or Publication Title: | Royal Statistical Society. Journal. Series B: Statistical Methodology | ||||
Publisher: | Wiley-Blackwell Publishing Ltd. | ||||
ISSN: | 1369-7412 | ||||
Official Date: | September 2008 | ||||
Dates: |
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Volume: | Volume 70 | ||||
Number: | Number 4 | ||||
Number of Pages: | 23 | ||||
Page Range: | pp. 755-777 | ||||
DOI: | 10.1111/j.1467-9868.2008.00661.x | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) |
Data sourced from Thomson Reuters' Web of Knowledge
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