Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/3902
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dc.contributor.authorDate, P-
dc.contributor.authorJalen, L-
dc.contributor.authorMamon, R-
dc.coverage.spatial16-
dc.date.accessioned2009-11-27T09:28:12Z-
dc.date.available2009-11-27T09:28:12Z-
dc.date.issued2010-
dc.identifier.citationJournal of Computational and Applied Mathematics. 233(10): 2675–2682, Mar 2010en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/3902-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0377042709007468en
dc.description.abstractA new technique for the latent state estimation of a wide class of nonlinear time series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment matching algorithm and then a linear programming-based procedure is used in the update step of the state estimation. The effectiveness of the new ¯ltering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process.en
dc.language.isoenen
dc.publisherElsevieren
dc.titleA partially linearized sigma point filter for latent state estimation in nonlinear time series modelsen
dc.typeResearch Paperen
dc.identifier.doihttp://dx.doi.org/10.1016/j.cam.2009.11.015-
Appears in Collections:Dept of Mathematics Research Papers
Mathematical Sciences

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