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|Title:||A partially linearized sigma point filter for latent state estimation in nonlinear time series models|
|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.|
|Appears in Collections:||Dept of Mathematics Research Papers|
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