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

DOI10.1016/j.cam.2009.11.015zbMath1181.62129OpenAlexW2132023324MaRDI QIDQ847249

Luka Jalen, Paresh Date, Rogemar S. Mamon

Publication date: 12 February 2010

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2009.11.015

zbMATH Keywords

state estimationnonlinear time seriessigma point filters

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of mathematical programming (90C90) Linear programming (90C05)

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Uses Software

• EnKF

Cites Work

• Data assimilation for magnetohydrodynamics systems
• Unscented Kalman filter for time varying spectral analysis of earthquake ground motions
• Different approaches for state filtering in nonlinear systems with uncertain observations
• Extended Kalman filters using explicit and derivative-free local linearizations
• A new moment matching algorithm for sampling from partially specified symmetric distributions
• A new algorithm for latent state estimation in non-linear time series models
• Stochastic processes and filtering theory
• A Theory of the Term Structure of Interest Rates
• Sequential Monte Carlo Methods for Dynamic Systems
• Local linearization filters for non-linear continuous-discrete state space models with multiplicative noise
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