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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3902

Title: A partially linearized sigma point filter for latent state estimation in nonlinear time series models
Authors: Date, P
Jalen, L
Mamon, R
Publication Date: 2010
Publisher: Elsevier
Citation: Journal of Computational and Applied Mathematics. 233(10): 2675–2682, Mar 2010
Abstract: A 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.
URI: http://bura.brunel.ac.uk/handle/2438/3902
http://www.sciencedirect.com/science/article/pii/S0377042709007468
DOI: http://dx.doi.org/10.1016/j.cam.2009.11.015
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Mathematical Science

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