A partially linearized sigma point filter for latent state estimation in nonlinear time series models

Authors:
Paresh Date;Luka Jalen;Rogemar Mamon
Affiliations:
Center for the Analysis of Risk and Optimization Modelling Applications, Department of Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, United Kingdom;Center for the Analysis of Risk and Optimization Modelling Applications, Department of Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, United Kingdom;Department of Statistical and Actuarial Sciences, The University of Western Ontario, London, Ontario, Canada N6A 5B7
Venue:
Journal of Computational and Applied Mathematics
Year:
2010

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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 filtering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process.